Flowchart¶
- Packages install and import
- Data import
- Weathering Indices (WIs) calculation and Compositional Space Diagrams design
- Data statistics - boxplots, scatter matrix, correlation matrix, correlation diagrams
- SedWeathering layout design
Packages installation¶
# uncomment below lines of code to install required packages
# !pip install dash
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install plotly_express
# !pip install dfply
Packages import¶
import base64
from base64 import b64encode
import datetime
import io
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
from dash.dependencies import Input, Output
from collections import OrderedDict
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# import plotnine
import dfply
from dfply import *
from plotnine import ggplot, aes
# package for plot scales
from mizani.formatters import comma_format
import ipywidgets as widgets
import warnings
warnings.filterwarnings('ignore')
from IPython.display import display
Data loading¶
Data (composite) details:
| Sample Name/ID | Sample Category/Subcategory | Reference |
|---|---|---|
| UCC-1 | Upper Continental Crust | Shaw et al., 1967, 1976, 1986 |
| UCC-2 | Upper Continental Crust | Fahrig and Eade, 1968; Eade and Fahrig, 1973 |
| UCC-3 | Upper Continental Crust | Ronov and Yaroshevskiy, 1976 |
| UCC-4 | Upper Continental Crust | Gao et al., 1998 |
| UCC-5 | Upper Continental Crust | Taylor and McLennan, 1985, 1995 |
| UCC-6 | Upper Continental Crust | Wedepohl, 1995 |
| UCC-7 | Upper Continental Crust | Rudnick and Gao, 2003 |
| UCC-8 | Upper Continental Crust | Togashi et al., 2000 |
| Dunite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Harzburgite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Peridotite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Lherzolite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Anorthosite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Gabbro | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Basalt | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Tholeiite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Andesite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Dacite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Tonalite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Diorite and Granodiorite quartz diorite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Granodiorite and quartz monzodiorite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Rhyolite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Granite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Trachyte | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Syenite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Nepheline syenite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| Phonolite | Igneous Composite | Hyndman, 1985; Le Maitre, 1976 |
| NASC | Shale Composite | McLennan et al., 1983; McLennan, 2009; Gromet et al., 1984 |
| PAAS | Shale Composite | Taylor and McLennan, 1985; Barth et al., 2000; Haskin and Haskin, 1966; McLennan, 2009; Nance and Taylor, 1976; McLennan, 1981, 1989 |
| shale | Shale Composite | Mason, 1982 |
| Amazon | Suspended Sediment | McLennan, 1993 |
| Colorado | Suspended Sediment | McLennan, 1993 |
| Columbia | Suspended Sediment | McLennan, 1993 |
| Congo (Zaire) | Suspended Sediment | McLennan, 1993 |
| Danube | Suspended Sediment | McLennan, 1993 |
| Gangess/Brahmaputra | Suspended Sediment | McLennan, 1993 |
| Huanghe | Suspended Sediment | McLennan, 1993 |
| Indus | Suspended Sediment | McLennan, 1993 |
| Mackenzie | Suspended Sediment | McLennan, 1993 |
| Mekong | Suspended Sediment | McLennan, 1993 |
| Mississippi | Suspended Sediment | McLennan, 1993 |
| Niger | Suspended Sediment | McLennan, 1993 |
| Nile | Suspended Sediment | McLennan, 1993 |
| Orinoco | Suspended Sediment | McLennan, 1993 |
| Parana | Suspended Sediment | McLennan, 1993 |
| St. Lawrence | Suspended Sediment | McLennan, 1993 |
| Western Europe | Denudation Region | McLennan, 1993 |
| Alpine Europe | Denudation Region | McLennan, 1993 |
| Central America (incl. Mexico) | Denudation Region | McLennan, 1993 |
| Northwest South America | Denudation Region | McLennan, 1993 |
| Ocean Islands (excl. New Zealand) | Denudation Region | McLennan, 1993 |
| New Zealand (South Island) | Denudation Region | McLennan, 1993 |
| U.S. Atlantic Coast | Denudation Region | McLennan, 1993 |
Acronym used:
Data (case study) details:
# Loading data (sedchem) from GitHub
# May be changed as per the individual repository
data = pd.read_csv('https://raw.githubusercontent.com/vinthegreat84/geochemistry/master/SedWeather/raw/sedchem.csv')
Weathering Indices (WIs) calculation and Compositional Space Diagrams¶
Molar calculation¶
data['molar_SiO2'] = data['SiO2']/60.08
data['molar_TiO2'] = data['TiO2']/79.866
data['molar_Al2O3'] = data['Al2O3']/101.9618
data['molar_Fe2O3'] = data['Fe2O3']/159.69
data['molar_MnO'] = data['MnO']/70.9374
data['molar_MgO'] = data['MgO']/40.3044
data['molar_CaO'] = data['CaO']/56.0794
data['molar_Na2O'] = data['Na2O']/61.97894
data['molar_K2O'] = data['K2O']/94.1954
data['molar_P2O5'] = data['P2O5']/141.944522
data['molar_CO2'] = data['CO2']/44.01
CaO* calculation¶
# After McLennan, 1993
data['diff'] = data['molar_CaO'] - (data['molar_CO2'] + data['molar_P2O5'])
data['molar_CaO*'] = np.where(data['diff'] < data['molar_Na2O'], data['diff'], data['molar_Na2O'])
Chemical Indices calculation¶
Weathering Indices (WIs) formulae used:¶
Chemical Index of Weathering (CIW) after Harnois, 1988:¶
# Chemical Index of Weathering (CIW) after Harnois, 1988
data['(CIW)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O'])
# Chemical Proxy of Alteration (CPA) after Buggle et al., 2011
data['(CPA)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_Na2O'])
# Chemical Index of Alteration (CIA) after Nesbitt and Young, 1982
data['(CIA)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O'])
# Plagioclase Index of Alteration (PIA) after Fedo et al., 1995
data['(PIA)'] = 100 * (data['molar_Al2O3'] - data['molar_K2O']) / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O']
- data['molar_K2O'])
# Modified Chemical Index of Alteration (CIX) after Garzanti et al., 2014
data['(CIX)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_Na2O'] + data['molar_K2O'])
# Index of Compositional Variability (ICV) after Cox et al., 1995
data['(ICV)'] = (data['Fe2O3'] + data['K2O'] + data['Na2O'] + data['CaO'] + data['MgO'] + data['MnO'] + data['TiO2']) / data['Al2O3']
# Weathering Index of Parker (WIP) after Parker, 1970
data['(WIP)'] = 100 * (2*data['molar_Na2O']/0.35 + data['molar_MgO']/0.9 + 2*data['molar_K2O']/0.25 + data['molar_CaO*']/0.7)
Compositional space diagrams¶
A - CN - K compositional space diagram after Nesbitt and Young, 1982¶
# Category plot
ACNK_cat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'], c=data['molar_K2O'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_cat.update_layout({'ternary': {'sum': 100}})
ACNK_cat.update_ternaries(bgcolor='yellow')
ACNK_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_cat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_cat.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_cat.write_html("category plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_cat, image='svg', filename='category plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_cat, image='jpeg', filename='category plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory plot
ACNK_subcat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'], c=data['molar_K2O'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_subcat.update_layout({'ternary': {'sum': 100}})
ACNK_subcat.update_ternaries(bgcolor='yellow')
ACNK_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_subcat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_subcat.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_subcat.write_html("subcategory plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_subcat, image='svg', filename='subcategory plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_subcat, image='jpeg', filename='subcategory plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
M - F - W compositional space diagramm after Ohta and Arai, 2007¶
# eight oxides (SiO2, TiO2, Al2O3, Fe2O3, MgO, CaO, Na2O and K2O) used in the formulas and re-closing to 100 wt.% (expressed as OXIDE.100 = 100*OXIDE/sum)
data['sum'] = data[['SiO2', 'TiO2', 'Al2O3', 'Fe2O3', 'MgO', 'CaO', 'Na2O', 'K2O']].sum(axis=1)
data['SiO2.100'] = 100 * data['SiO2'] / data['sum']
data['TiO2.100'] = 100 * data['TiO2'] / data['sum']
data['Al2O3.100'] = 100 * data['Al2O3'] / data['sum']
data['Fe2O3.100'] = 100 * data['Fe2O3'] / data['sum']
data['MgO.100'] = 100 * data['MgO'] / data['sum']
data['CaO.100'] = 100 * data['CaO'] / data['sum']
data['Na2O.100'] = 100 * data['Na2O'] / data['sum']
data['K2O.100'] = 100 * data['K2O'] / data['sum']
# M - F - W apices calculation after Ohta and Arai, 2007
## Step 1 of the formulas for calculating vertices (M, F and W) of M - F - W compositional space diagram
data['(M)'] = - 0.395*np.log(data['SiO2.100']) + 0.206*np.log(data['TiO2.100']) - 0.316*np.log(data['Al2O3.100']) + 0.160*np.log(data['Fe2O3.100']) + 0.246*np.log(data['MgO.100']) + 0.368*np.log(data['CaO.100']) + 0.073*np.log(data['Na2O.100']) - 0.342*np.log(data['K2O.100']) + 2.266
data['(F)'] = + 0.191*np.log(data['SiO2.100']) - 0.397*np.log(data['TiO2.100']) + 0.020*np.log(data['Al2O3.100']) - 0.375*np.log(data['Fe2O3.100']) - 0.243*np.log(data['MgO.100']) + 0.079*np.log(data['CaO.100']) + 0.392*np.log(data['Na2O.100']) + 0.333*np.log(data['K2O.100']) - 0.892
data['(W)'] = + 0.203*np.log(data['SiO2.100']) + 0.191*np.log(data['TiO2.100']) + 0.296*np.log(data['Al2O3.100']) + 0.215*np.log(data['Fe2O3.100']) - 0.002*np.log(data['MgO.100']) - 0.448*np.log(data['CaO.100']) - 0.464*np.log(data['Na2O.100']) + 0.008*np.log(data['K2O.100']) - 1.374
## Step 2 of the formulas for calculating vertices (M, F and W) of M - F - W compositional space diagram
data['(M)'] = np.exp(data['(M)'])
data['(F)'] = np.exp(data['(F)'])
data['(W)'] = np.exp(data['(W)'])
# ternary plot drawing
MFW_cat = px.scatter_ternary(data, a=data['(M)'], b=data['(F)'], c=data['(W)'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_cat.update_layout({'ternary': {'sum': 100}})
MFW_cat.update_ternaries(bgcolor='yellow')
MFW_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_cat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_cat.show()
# uncomment below lines of code to export the plot to your local machine
# MFW_cat.write_html("category plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_cat, image='svg', filename='category plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_cat, image='jpeg', filename='category plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory plot
MFW_subcat = px.scatter_ternary(data, a=data['(M)'], b=data['(F)'], c=data['(W)'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_subcat.update_layout({'ternary': {'sum': 100}})
MFW_subcat.update_ternaries(bgcolor='yellow')
MFW_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_subcat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_subcat.show()
# uncomment below lines of code to export the plot to your local machine
# MFW_subcat.write_html("subcategory plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_subcat, image='svg', filename='subcategory plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_subcat, image='jpeg', filename='subcategory plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
A - CNK - FM compositional space diagram after Nesbitt and Young, 1989¶
# Category plot
ACNKFM_cat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O'], c=data['molar_Fe2O3'] + data['molar_MgO'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_cat.update_layout({'ternary': {'sum': 100}})
ACNKFM_cat.update_ternaries(bgcolor='yellow')
ACNKFM_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_cat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_cat.show()
# uncomment below lines of code to export the plot to your local machine
# ACNKFM_cat.write_html("category plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_cat, image='svg', filename='category plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_cat, image='jpeg', filename='category plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory plot
ACNKFM_subcat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O'], c=data['molar_Fe2O3'] + data['molar_MgO'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_subcat.update_layout({'ternary': {'sum': 100}})
ACNKFM_subcat.update_ternaries(bgcolor='yellow')
ACNKFM_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_subcat.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_subcat.show()
# uncomment below lines of code to export the plot to your local machine
# ACNKFM_subcat.write_html("subcategory plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_subcat, image='svg', filename='subcategory plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_subcat, image='jpeg', filename='subcategory plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
Data slicing¶
# filtering data into subcategories
# filtering data into subcategory (upper_continental_crust)
data_upper_continental_crust = data[data.subcategory == 'upper_continental_crust']
# filtering data into subcategory (igneous_composite)
data_igneous_composite = data[data.subcategory == 'igneous_composite']
# filtering data into subcategory (shale_composite)
data_shale_composite = data[data.subcategory == 'shale_composite']
# filtering data into subcategories (UCC, river_sediment, region)
data_UCC_rs_r = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == 'river_sediment') | (data["subcategory"] == 'region')]
# filtering data into subcategory (UCC, Manasbal_Lake)
data_UCC_ml = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Manasbal_Lake")]
# filtering data into subcategory (UCC, Surma_Group, Barail_Group)
data_UCC_sg_bg = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Surma_Group") | (data["subcategory"] == "Barail_Group")]
# filtering data into subcategory (UCC, channel_sediments, overbank_sediments, suspended_sediments)
data_UCC_cs_os_ss = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "channel_sediments") | (data["subcategory"] == "overbank_sediments") | (data["subcategory"] == "suspended_sediments")]
# filtering data into subcategory (UCC, Silty_clay, Sand)
data_UCC_sc_s = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Silty_clay") | (data["subcategory"] == "Sand")]
user-defined subcategory and subsubcategory plots¶
# Subcategory ('UCC') plots
# A - CN - K compositional space diagram
ACNK_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['molar_Al2O3'], b=data_upper_continental_crust['molar_CaO*'] + data_upper_continental_crust['molar_Na2O'], c=data_upper_continental_crust['molar_K2O'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
ACNK_upper_continental_crust.update_ternaries(bgcolor='yellow')
ACNK_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_upper_continental_crust.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_upper_continental_crust.show()
# M - F - W compositional space diagram
MFW_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['(M)'], b=data_upper_continental_crust['(F)'], c=data_upper_continental_crust['(W)'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
MFW_upper_continental_crust.update_ternaries(bgcolor='yellow')
MFW_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_upper_continental_crust.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_upper_continental_crust.show()
# A - CNK - FM compositional space diagram
ACNKFM_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['molar_Al2O3'], b=data_upper_continental_crust['molar_CaO*'] + data_upper_continental_crust['molar_Na2O'] + data_upper_continental_crust['molar_K2O'], c=data_upper_continental_crust['molar_Fe2O3'] + data_upper_continental_crust['molar_MgO'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
ACNKFM_upper_continental_crust.update_ternaries(bgcolor='yellow')
ACNKFM_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_upper_continental_crust.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_upper_continental_crust.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_upper_continental_crust.write_html("upper_continental_crust plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_upper_continental_crust, image='svg', filename='upper_continental_crust plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_upper_continental_crust.write_html("upper_continental_crust plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_upper_continental_crust, image='svg', filename='upper_continental_crust plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_upper_continental_crust.write_html("upper_continental_crust plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_upper_continental_crust, image='svg', filename='upper_continental_crust plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('igneous_composite') plots
# A - CN - K compositional space diagram
ACNK_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['molar_Al2O3'], b=data_igneous_composite['molar_CaO*'] + data_igneous_composite['molar_Na2O'], c=data_igneous_composite['molar_K2O'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_igneous_composite.update_layout({'ternary': {'sum': 100}})
ACNK_igneous_composite.update_ternaries(bgcolor='yellow')
ACNK_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_igneous_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_igneous_composite.show()
# M - F - W compositional space diagram
MFW_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['(M)'], b=data_igneous_composite['(F)'], c=data_igneous_composite['(W)'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_igneous_composite.update_layout({'ternary': {'sum': 100}})
MFW_igneous_composite.update_ternaries(bgcolor='yellow')
MFW_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_igneous_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_igneous_composite.show()
# A - CNK - FM compositional space diagram
ACNKFM_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['molar_Al2O3'], b=data_igneous_composite['molar_CaO*'] + data_igneous_composite['molar_Na2O'] + data_igneous_composite['molar_K2O'], c=data_igneous_composite['molar_Fe2O3'] + data_igneous_composite['molar_MgO'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_igneous_composite.update_layout({'ternary': {'sum': 100}})
ACNKFM_igneous_composite.update_ternaries(bgcolor='yellow')
ACNKFM_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_igneous_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_igneous_composite.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_igneous_composite.write_html("igneous_composite plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_igneous_composite, image='svg', filename='igneous_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_igneous_composite, image='jpeg', filename='igneous_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_igneous_composite.write_html("igneous_composite plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_igneous_composite, image='svg', filename='igneous_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_igneous_composite, image='jpeg', filename='igneous_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_igneous_composite.write_html("igneous_composite plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_igneous_composite, image='svg', filename='igneous_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_igneous_composite, image='jpeg', filename='igneous_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('shale_composite') plots
# A - CN - K compositional space diagram
ACNK_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['molar_Al2O3'], b=data_shale_composite['molar_CaO*'] + data_shale_composite['molar_Na2O'], c=data_shale_composite['molar_K2O'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_shale_composite.update_layout({'ternary': {'sum': 100}})
ACNK_shale_composite.update_ternaries(bgcolor='yellow')
ACNK_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_shale_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_shale_composite.show()
# M - F - W compositional space diagram
MFW_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['(M)'], b=data_shale_composite['(F)'], c=data_shale_composite['(W)'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_shale_composite.update_layout({'ternary': {'sum': 100}})
MFW_shale_composite.update_ternaries(bgcolor='yellow')
MFW_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_shale_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_shale_composite.show()
# A - CNK - FM compositional space diagram
ACNKFM_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['molar_Al2O3'], b=data_shale_composite['molar_CaO*'] + data_shale_composite['molar_Na2O'] + data_shale_composite['molar_K2O'], c=data_shale_composite['molar_Fe2O3'] + data_shale_composite['molar_MgO'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_shale_composite.update_layout({'ternary': {'sum': 100}})
ACNKFM_shale_composite.update_ternaries(bgcolor='yellow')
ACNKFM_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_shale_composite.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_shale_composite.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_shale_composite.write_html("shale_composite plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_shale_composite, image='svg', filename='shale_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_shale_composite, image='jpeg', filename='shale_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_shale_composite.write_html("shale_composite plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_shale_composite, image='svg', filename='shale_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_shale_composite, image='jpeg', filename='shale_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_shale_composite.write_html("shale_composite plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_shale_composite, image='svg', filename='shale_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_shale_composite, image='jpeg', filename='shale_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('UCC', 'river_sediment', 'region') plots
# A - CN - K compositional space diagram
ACNK_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['molar_Al2O3'], b=data_UCC_rs_r['molar_CaO*'] + data_UCC_rs_r['molar_Na2O'], c=data_UCC_rs_r['molar_K2O'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_rs_r.update_ternaries(bgcolor='yellow')
ACNK_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_UCC_rs_r.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_UCC_rs_r.show()
# M - F - W compositional space diagram
MFW_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['(M)'], b=data_UCC_rs_r['(F)'], c=data_UCC_rs_r['(W)'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
MFW_UCC_rs_r.update_ternaries(bgcolor='yellow')
MFW_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_UCC_rs_r.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_UCC_rs_r.show()
# A - CNK - FM compositional space diagram
ACNKFM_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['molar_Al2O3'], b=data_UCC_rs_r['molar_CaO*'] + data_UCC_rs_r['molar_Na2O'] + data_UCC_rs_r['molar_K2O'], c=data_UCC_rs_r['molar_Fe2O3'] + data_UCC_rs_r['molar_MgO'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_rs_r.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_UCC_rs_r.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_UCC_rs_r.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_rs_r.write_html("UCC_rs_r plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_rs_r, image='svg', filename='UCC_rs_r plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_UCC_rs_r.write_html("UCC_rs_r plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_rs_r, image='svg', filename='UCC_rs_r plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_UCC_rs_r.write_html("UCC_rs_r plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_rs_r, image='svg', filename='UCC_rs_r plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('UCC', Manasbal_Lake') plots
# A - CN - K compositional space diagram
ACNK_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['molar_Al2O3'], b=data_UCC_ml['molar_CaO*'] + data_UCC_ml['molar_Na2O'], c=data_UCC_ml['molar_K2O'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_UCC_ml.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_ml.update_ternaries(bgcolor='yellow')
ACNK_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_UCC_ml.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_UCC_ml.show()
# M - F - W compositional space diagram
MFW_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['(M)'], b=data_UCC_ml['(F)'], c=data_UCC_ml['(W)'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_UCC_ml.update_layout({'ternary': {'sum': 100}})
MFW_UCC_ml.update_ternaries(bgcolor='yellow')
MFW_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_UCC_ml.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_UCC_ml.show()
# A - CNK - FM compositional space diagram
ACNKFM_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['molar_Al2O3'], b=data_UCC_ml['molar_CaO*'] + data_UCC_ml['molar_Na2O'] + data_UCC_ml['molar_K2O'], c=data_UCC_ml['molar_Fe2O3'] + data_UCC_ml['molar_MgO'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_UCC_ml.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_ml.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_UCC_ml.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_UCC_ml.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_ml.write_html("UCC_ml plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_ml, image='svg', filename='UCC_ml plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_ml, image='jpeg', filename='UCC_ml plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_UCC_ml.write_html("UCC_ml plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_ml, image='svg', filename='UCC_ml plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_ml, image='jpeg', filename='UCC_ml plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_UCC_ml.write_html("UCC_ml plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_ml, image='svg', filename='UCC_ml plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_ml, image='jpeg', filename='UCC_ml plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('UCC', 'Surma_Group', 'Barail_Group') plots
# A - CN - K compositional space diagram
ACNK_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['molar_Al2O3'], b=data_UCC_sg_bg['molar_CaO*'] + data_UCC_sg_bg['molar_Na2O'], c=data_UCC_sg_bg['molar_K2O'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_sg_bg.update_ternaries(bgcolor='yellow')
ACNK_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_UCC_sg_bg.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_UCC_sg_bg.show()
# M - F - W compositional space diagram
MFW_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['(M)'], b=data_UCC_sg_bg['(F)'], c=data_UCC_sg_bg['(W)'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
MFW_UCC_sg_bg.update_ternaries(bgcolor='yellow')
MFW_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_UCC_sg_bg.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_UCC_sg_bg.show()
# A - CNK - FM compositional space diagram
ACNKFM_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['molar_Al2O3'], b=data_UCC_sg_bg['molar_CaO*'] + data_UCC_sg_bg['molar_Na2O'] + data_UCC_sg_bg['molar_K2O'], c=data_UCC_sg_bg['molar_Fe2O3'] + data_UCC_sg_bg['molar_MgO'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_sg_bg.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_UCC_sg_bg.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_UCC_sg_bg.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_sg_bg.write_html("UCC_sg_bg plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_UCC_sg_bg.write_html("UCC_sg_bg plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_UCC_sg_bg.write_html("UCC_sg_bg plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('UCC', channel_sediments', overbank_sediments', suspended_sediments') plots
# A - CN - K compositional space diagram
ACNK_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['molar_Al2O3'], b=data_UCC_cs_os_ss['molar_CaO*'] + data_UCC_cs_os_ss['molar_Na2O'], c=data_UCC_cs_os_ss['molar_K2O'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
ACNK_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_UCC_cs_os_ss.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_UCC_cs_os_ss.show()
# M - F - W compositional space diagram
MFW_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['(M)'], b=data_UCC_cs_os_ss['(F)'], c=data_UCC_cs_os_ss['(W)'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
MFW_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
MFW_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_UCC_cs_os_ss.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_UCC_cs_os_ss.show()
# A - CNK - FM compositional space diagram
ACNKFM_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['molar_Al2O3'], b=data_UCC_cs_os_ss['molar_CaO*'] + data_UCC_cs_os_ss['molar_Na2O'] + data_UCC_cs_os_ss['molar_K2O'], c=data_UCC_cs_os_ss['molar_Fe2O3'] + data_UCC_cs_os_ss['molar_MgO'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_UCC_cs_os_ss.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_UCC_cs_os_ss.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
# Subcategory ('UCC', Silty_clay', 'Sand') plots
# A - CN - K compositional space diagram
ACNK_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['molar_Al2O3'], b=data_UCC_sc_s['molar_CaO*'] + data_UCC_sc_s['molar_Na2O'], c=data_UCC_sc_s['molar_K2O'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNK_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_sc_s.update_ternaries(bgcolor='yellow')
ACNK_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNK_UCC_sc_s.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
'caxis':{'title': 'K<sub>2</sub>O'}
}
})
ACNK_UCC_sc_s.show()
# M - F - W compositional space diagram
MFW_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['(M)'], b=data_UCC_sc_s['(F)'], c=data_UCC_sc_s['(W)'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
MFW_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
MFW_UCC_sc_s.update_ternaries(bgcolor='yellow')
MFW_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
MFW_UCC_sc_s.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'M'},
'baxis':{'title': 'F'},
'caxis':{'title': 'W'}
}
})
MFW_UCC_sc_s.show()
# A - CNK - FM compositional space diagram
ACNKFM_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['molar_Al2O3'], b=data_UCC_sc_s['molar_CaO*'] + data_UCC_sc_s['molar_Na2O'] + data_UCC_sc_s['molar_K2O'], c=data_UCC_sc_s['molar_Fe2O3'] + data_UCC_sc_s['molar_MgO'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)
ACNKFM_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_sc_s.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))
ACNKFM_UCC_sc_s.update_layout({
'ternary':
{
'sum':100,
'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
}
})
ACNKFM_UCC_sc_s.show()
# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_sc_s.write_html("UCC_sc_s plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_sc_s, image='svg', filename='UCC_sc_s plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version
# MFW_UCC_sc_s.write_html("UCC_sc_s plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_sc_s, image='svg', filename='UCC_sc_s plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version
# ACNKFM_UCC_sc_s.write_html("UCC_sc_s plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_sc_s, image='svg', filename='UCC_sc_s plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version
Ratios¶
data['SiO2/Al2O3'] = data['SiO2'] / data['Al2O3']
data['K2O/Al2O3'] = data['K2O'] / data['Al2O3']
data['Al2O3/TiO2'] = data['Al2O3'] / data['TiO2']
# Normalization of 'M', 'F' and 'W' values to 100
data['(M)'] = 100 * data['(M)'] / (data['(M)'] + data['(F)'] + data['(W)'])
data['(F)'] = 100 * data['(F)'] / (data['(M)'] + data['(F)'] + data['(W)'])
data['(W)'] = 100 - (data['(M)'] + data['(F)'])
Data statistics¶
Dropping extra variable and subsetting data¶
data = data.drop(['CO2', 'molar_SiO2', 'molar_TiO2', 'molar_Al2O3', 'molar_Fe2O3', 'molar_MnO', 'molar_MgO', 'molar_CaO',
'molar_Na2O', 'molar_K2O', 'molar_P2O5', 'molar_CO2', 'diff', 'molar_CaO*', 'sum', 'SiO2.100', 'TiO2.100',
'Al2O3.100', 'Fe2O3.100', 'MgO.100', 'CaO.100', 'Na2O.100', 'K2O.100', '(M)', '(F)'], axis=1)
data = data.round(2)
# exporting csv file of raw data along with calculated weathering indices to your local machine
data.to_csv('data.csv', index = False, float_format='%.2f')
Boxplots¶
box_CIW = px.box(data, x="subcategory", y="(CIW)", color="subsubcategory")
box_CIW.update_layout(
xaxis_title='sample subcategory',
yaxis_title='CIW',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_CIW.write_html("Boxplot of CIW variation.html") # for 'html' version
# iplot(box_CIW, image='svg', filename='Boxplot of CIW variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIW, image='jpeg', filename='Boxplot of CIW variation', image_width=800, image_height=800) # for 'jpeg' version
box_CPA = px.box(data, x="subcategory", y="(CPA)", color="subsubcategory")
box_CPA.update_layout(
xaxis_title='sample subcategory',
yaxis_title='CPA',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_CPA.write_html("Boxplot of CPA variation.html") # for 'html' version
# iplot(box_CPA, image='svg', filename='Boxplot of CPA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CPA, image='jpeg', filename='Boxplot of CPA variation', image_width=800, image_height=800) # for 'jpeg' version
box_CIA = px.box(data, x="subcategory", y="(CIA)", color="subsubcategory")
box_CIA.update_layout(
xaxis_title='sample subcategory',
yaxis_title='CIA',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_CIA.write_html("Boxplot of CIA variation.html") # for 'html' version
# iplot(box_CIA, image='svg', filename='Boxplot of CIA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIA, image='jpeg', filename='Boxplot of CIA variation', image_width=800, image_height=800) # for 'jpeg' version
box_PIA = px.box(data, x="subcategory", y="(PIA)", color="subsubcategory")
box_PIA.update_layout(
xaxis_title='sample subcategory',
yaxis_title='PIA',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_PIA.write_html("Boxplot of PIA variation.html") # for 'html' version
# iplot(box_PIA, image='svg', filename='Boxplot of PIA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_PIA, image='jpeg', filename='Boxplot of PIA variation', image_width=800, image_height=800) # for 'jpeg' version
box_CIX = px.box(data, x="subcategory", y="(CIX)", color="subsubcategory")
box_CIX.update_layout(
xaxis_title='sample subcategory',
yaxis_title='CIX',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_CIX.write_html("Boxplot of CIX variation.html") # for 'html' version
# iplot(box_CIX, image='svg', filename='Boxplot of CIX variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIX, image='jpeg', filename='Boxplot of CIX variation', image_width=800, image_height=800) # for 'jpeg' version
box_ICV = px.box(data, x="subcategory", y="(ICV)", color="subsubcategory")
box_ICV.update_layout(
xaxis_title='sample subcategory',
yaxis_title='ICV',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_ICV.write_html("Boxplot of ICV variation.html") # for 'html' version
# iplot(box_ICV, image='svg', filename='Boxplot of ICV variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_ICV, image='jpeg', filename='Boxplot of ICV variation', image_width=800, image_height=800) # for 'jpeg' version
box_WIP = px.box(data, x="subcategory", y="(WIP)", color="subsubcategory")
box_WIP.update_layout(
xaxis_title='sample subcategory',
yaxis_title='WIP',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_WIP.write_html("Boxplot of WIP variation.html") # for 'html' version
# iplot(box_WIP, image='svg', filename='Boxplot of WIP variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_WIP, image='jpeg', filename='Boxplot of WIP variation', image_width=800, image_height=800) # for 'jpeg' version
box_W = px.box(data, x="subcategory", y="(W)", color="subsubcategory")
box_W.update_layout(
xaxis_title='sample subcategory',
yaxis_title='W',
showlegend=False
)
# uncomment below lines of code to export the plot to your local machine
# box_W.write_html("Boxplot of W variation.html") # for 'html' version
# iplot(box_W, image='svg', filename='Boxplot of W variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_W, image='jpeg', filename='Boxplot of W variation', image_width=800, image_height=800) # for 'jpeg' version
Scatter matrix¶
# filtering data into subcategories ('Manasbal_Lake', 'Surma_Group', 'Barail_Group', 'channel_sediments', 'overbank_sediments', 'suspended_sediments', 'Silty_clay', 'Sand')
data_ml = data[data.subcategory == 'Manasbal_Lake']
data_sg = data[data.subcategory == 'Surma_Group']
data_bg = data[data.subcategory == 'Barail_Group']
data_cs = data[data.subcategory == 'channel_sediments']
data_os = data[data.subcategory == 'overbank_sediments']
data_ss = data[data.subcategory == 'suspended_sediments']
data_sc = data[data.subcategory == 'Silty_clay']
data_s = data[data.subcategory == 'Sand']
# scatter matrix of weathering indices of subcategories
# scatter matrix of weathering indices of subsubcategory ('Manasbal_Lake')
scatter_ml = px.scatter_matrix(data_ml, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_ml.show()
# scatter matrix of weathering indices of subsubcategory ('Surma_Group')
scatter_sg = px.scatter_matrix(data_sg, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_sg.show()
# scatter matrix of weathering indices of subsubcategory ('Barail_Group')
scatter_bg = px.scatter_matrix(data_bg, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_bg.show()
# scatter matrix of weathering indices of subsubcategory ('channel_sediments')
scatter_cs = px.scatter_matrix(data_cs, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_cs.show()
# scatter matrix of weathering indices of subsubcategory ('overbank_sediments')
scatter_os = px.scatter_matrix(data_os, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_os.show()
# scatter matrix of weathering indices of subsubcategory ('suspended_sediments')
scatter_ss = px.scatter_matrix(data_ss, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_ss.show()
# scatter matrix of weathering indices of subsubcategory ('Silty_clay')
scatter_sc = px.scatter_matrix(data_sc, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_sc.show()
# scatter matrix of weathering indices of subsubcategory ('Sand')
scatter_s = px.scatter_matrix(data_s, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_s.show()
# uncomment below lines of code to export the plot to your local machine
# scatter_ml.write_html("scatter matrix of weathering indices of Manasbal Lake.html") # for 'html' version
# iplot(scatter_ml, image='svg', filename='scatter matrix of weathering indices of Manasbal Lake', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_ml, image='jpeg', filename='scatter matrix of weathering indices of Manasbal Lake', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_sg.write_html("scatter matrix of weathering indices of Surma Group.html") # for 'html' version
# iplot(scatter_sg, image='svg', filename='scatter matrix of weathering indices of Surma Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_sg, image='jpeg', filename='scatter matrix of weathering indices of Surma Group', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_bg.write_html("scatter matrix of weathering indices of Barail Group.html") # for 'html' version
# iplot(scatter_bg, image='svg', filename='scatter matrix of weathering indices of Barail Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_bg, image='jpeg', filename='scatter matrix of weathering indices of Barail Group', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_cs.write_html("scatter matrix of weathering indices of channel_sediments.html") # for 'html' version
# iplot(scatter_cs, image='svg', filename='scatter matrix of weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_cs, image='jpeg', filename='scatter matrix of weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_os.write_html("scatter matrix of weathering indices of overbank_sediments.html") # for 'html' version
# iplot(scatter_os, image='svg', filename='scatter matrix of weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_os, image='jpeg', filename='scatter matrix of weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_ss.write_html("scatter matrix of weathering indices of suspended_sediments.html") # for 'html' version
# iplot(scatter_ss, image='svg', filename='scatter matrix of weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_ss, image='jpeg', filename='scatter matrix of weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_sc.write_html("scatter matrix of weathering indices of Silty_clay.html") # for 'html' version
# iplot(scatter_sc, image='svg', filename='scatter matrix of weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_sc, image='jpeg', filename='scatter matrix of weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'jpeg' version
# scatter_s.write_html("scatter matrix of weathering indices of Sand.html") # for 'html' version
# iplot(scatter_s, image='svg', filename='scatter matrix of weathering indices of Sand', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_s, image='jpeg', filename='scatter matrix of weathering indices of Sand', image_width=1500, image_height=600) # for 'jpeg' version
Correlation matrix¶
# correlation matrix of subcategories
# correlation matrix of subcategory ('Manasbal_Lake')
corrMatrix_ml = round(data_ml.corr(), 2)
# correlation matrix of subcategory ('Surma_Group')
corrMatrix_sg = round(data_sg.corr(), 2)
# correlation matrix of subcategory ('Barail_Group')
corrMatrix_bg = round(data_bg.corr(), 2)
# correlation matrix of subcategory ('channel_sediments')
corrMatrix_cs = round(data_cs.corr(), 2)
# correlation matrix of subcategory ('overbank_sediments')
corrMatrix_os = round(data_os.corr(), 2)
# correlation matrix of subcategory ('suspended_sediments')
corrMatrix_ss = round(data_ss.corr(), 2)
# correlation matrix of subcategory ('Silty_clay')
corrMatrix_sc = round(data_sc.corr(), 2)
# correlation matrix of subcategory ('Sand')
corrMatrix_s = round(data_s.corr(), 2)
# Uncomment the below lines to export the data file to your local machine
# corrMatrix_ml.to_csv('corrMatrix_ml.csv', float_format='%.2f')
# corrMatrix_sg.to_csv('corrMatrix_sg.csv', float_format='%.2f')
# corrMatrix_bg.to_csv('corrMatrix_bg.csv', float_format='%.2f')
# corrMatrix_cs.to_csv('corrMatrix_cs.csv', float_format='%.2f')
# corrMatrix_os.to_csv('corrMatrix_os.csv', float_format='%.2f')
# corrMatrix_ss.to_csv('corrMatrix_ss.csv', float_format='%.2f')
# corrMatrix_sc.to_csv('corrMatrix_sc.csv', float_format='%.2f')
# corrMatrix_s.to_csv('corrMatrix_s.csv', float_format='%.2f')
Visual representation of correlation matrix of subcategories¶
# Visual representation of correlation matrix of subsubcategory ('Manasbal_Lake')
corrMatrix_ml.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.02 | 0.35 | 0.19 | -0.19 | 0.08 | -0.68 | 0.58 | -0.11 | 0.55 | -0.10 | 0.34 | 0.33 | 0.32 | 0.31 | 0.30 | -0.42 | -0.73 | 0.56 | -0.16 | 0.25 | 0.36 |
| TiO2 | -0.02 | 1.00 | 0.17 | 0.29 | 0.38 | -0.04 | 0.00 | -0.16 | -0.06 | -0.02 | -0.08 | 0.19 | 0.19 | 0.21 | 0.22 | 0.23 | -0.08 | -0.02 | 0.01 | -0.20 | -0.30 | -0.10 |
| Al2O3 | 0.35 | 0.17 | 1.00 | 0.47 | 0.41 | 0.79 | -0.55 | 0.66 | 0.23 | 0.85 | 0.32 | 0.72 | 0.73 | 0.74 | 0.75 | 0.76 | -0.92 | -0.40 | 0.77 | -0.95 | -0.31 | 0.96 |
| Fe2O3 | 0.19 | 0.29 | 0.47 | 1.00 | 0.77 | 0.40 | -0.35 | 0.42 | -0.09 | 0.48 | 0.09 | 0.41 | 0.39 | 0.41 | 0.42 | 0.40 | -0.35 | -0.37 | 0.11 | -0.37 | -0.06 | 0.38 |
| FeO | -0.19 | 0.38 | 0.41 | 0.77 | 1.00 | 0.38 | 0.03 | 0.19 | 0.00 | 0.28 | 0.13 | 0.37 | 0.37 | 0.38 | 0.40 | 0.39 | -0.28 | 0.07 | -0.06 | -0.46 | -0.22 | 0.31 |
| MnO | 0.08 | -0.04 | 0.79 | 0.40 | 0.38 | 1.00 | -0.44 | 0.64 | 0.19 | 0.78 | 0.56 | 0.59 | 0.61 | 0.60 | 0.59 | 0.61 | -0.78 | -0.30 | 0.55 | -0.82 | 0.01 | 0.81 |
| MgO | -0.68 | 0.00 | -0.55 | -0.35 | 0.03 | -0.44 | 1.00 | -0.68 | -0.26 | -0.64 | -0.33 | -0.25 | -0.25 | -0.25 | -0.24 | -0.24 | 0.72 | 0.91 | -0.71 | 0.42 | -0.11 | -0.55 |
| CaO | 0.58 | -0.16 | 0.66 | 0.42 | 0.19 | 0.64 | -0.68 | 1.00 | 0.25 | 0.75 | 0.36 | 0.40 | 0.40 | 0.39 | 0.39 | 0.39 | -0.65 | -0.55 | 0.46 | -0.52 | 0.13 | 0.71 |
| Na2O | -0.11 | -0.06 | 0.23 | -0.09 | 0.00 | 0.19 | -0.26 | 0.25 | 1.00 | -0.07 | 0.63 | -0.49 | -0.48 | -0.46 | -0.45 | -0.43 | -0.17 | 0.15 | 0.12 | -0.15 | -0.46 | 0.25 |
| K2O | 0.55 | -0.02 | 0.85 | 0.48 | 0.28 | 0.78 | -0.64 | 0.75 | -0.07 | 1.00 | 0.21 | 0.80 | 0.80 | 0.79 | 0.77 | 0.78 | -0.87 | -0.61 | 0.80 | -0.80 | 0.23 | 0.87 |
| P2O5 | -0.10 | -0.08 | 0.32 | 0.09 | 0.13 | 0.56 | -0.33 | 0.36 | 0.63 | 0.21 | 1.00 | -0.12 | -0.11 | -0.12 | -0.12 | -0.09 | -0.32 | -0.06 | 0.23 | -0.30 | -0.09 | 0.35 |
| (CIW) | 0.34 | 0.19 | 0.72 | 0.41 | 0.37 | 0.59 | -0.25 | 0.40 | -0.49 | 0.80 | -0.12 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | -0.70 | -0.40 | 0.59 | -0.76 | 0.07 | 0.68 |
| (CPA) | 0.33 | 0.19 | 0.73 | 0.39 | 0.37 | 0.61 | -0.25 | 0.40 | -0.48 | 0.80 | -0.11 | 1.00 | 1.00 | 1.00 | 0.99 | 0.99 | -0.71 | -0.39 | 0.60 | -0.77 | 0.08 | 0.69 |
| (CIA) | 0.32 | 0.21 | 0.74 | 0.41 | 0.38 | 0.60 | -0.25 | 0.39 | -0.46 | 0.79 | -0.12 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | -0.72 | -0.38 | 0.59 | -0.78 | 0.02 | 0.70 |
| (PIA) | 0.31 | 0.22 | 0.75 | 0.42 | 0.40 | 0.59 | -0.24 | 0.39 | -0.45 | 0.77 | -0.12 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | -0.71 | -0.37 | 0.59 | -0.79 | -0.03 | 0.70 |
| (CIX) | 0.30 | 0.23 | 0.76 | 0.40 | 0.39 | 0.61 | -0.24 | 0.39 | -0.43 | 0.78 | -0.09 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | -0.73 | -0.36 | 0.60 | -0.81 | -0.02 | 0.71 |
| (ICV) | -0.42 | -0.08 | -0.92 | -0.35 | -0.28 | -0.78 | 0.72 | -0.65 | -0.17 | -0.87 | -0.32 | -0.70 | -0.71 | -0.72 | -0.71 | -0.73 | 1.00 | 0.61 | -0.86 | 0.92 | 0.11 | -0.91 |
| (WIP) | -0.73 | -0.02 | -0.40 | -0.37 | 0.07 | -0.30 | 0.91 | -0.55 | 0.15 | -0.61 | -0.06 | -0.40 | -0.39 | -0.38 | -0.37 | -0.36 | 0.61 | 1.00 | -0.63 | 0.29 | -0.30 | -0.39 |
| (W) | 0.56 | 0.01 | 0.77 | 0.11 | -0.06 | 0.55 | -0.71 | 0.46 | 0.12 | 0.80 | 0.23 | 0.59 | 0.60 | 0.59 | 0.59 | 0.60 | -0.86 | -0.63 | 1.00 | -0.72 | 0.01 | 0.78 |
| SiO2/Al2O3 | -0.16 | -0.20 | -0.95 | -0.37 | -0.46 | -0.82 | 0.42 | -0.52 | -0.15 | -0.80 | -0.30 | -0.76 | -0.77 | -0.78 | -0.79 | -0.81 | 0.92 | 0.29 | -0.72 | 1.00 | 0.28 | -0.91 |
| K2O/Al2O3 | 0.25 | -0.30 | -0.31 | -0.06 | -0.22 | 0.01 | -0.11 | 0.13 | -0.46 | 0.23 | -0.09 | 0.07 | 0.08 | 0.02 | -0.03 | -0.02 | 0.11 | -0.30 | 0.01 | 0.28 | 1.00 | -0.22 |
| Al2O3/TiO2 | 0.36 | -0.10 | 0.96 | 0.38 | 0.31 | 0.81 | -0.55 | 0.71 | 0.25 | 0.87 | 0.35 | 0.68 | 0.69 | 0.70 | 0.70 | 0.71 | -0.91 | -0.39 | 0.78 | -0.91 | -0.22 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('Surma_Group')
corrMatrix_sg.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.96 | -0.82 | -0.42 | -0.72 | -0.65 | -0.68 | 0.65 | -0.90 | -0.51 | -0.73 | 0.59 | 0.58 | 0.56 | 0.53 | 0.52 | -0.48 | -0.98 | 0.66 | 0.93 | 0.40 | 0.64 |
| TiO2 | -0.96 | 1.00 | 0.88 | 0.30 | 0.62 | 0.68 | 0.49 | -0.53 | 0.95 | 0.45 | 0.61 | -0.58 | -0.57 | -0.54 | -0.49 | -0.47 | 0.39 | 0.94 | -0.47 | -0.95 | -0.51 | -0.62 |
| Al2O3 | -0.82 | 0.88 | 1.00 | 0.27 | 0.56 | 0.43 | 0.20 | -0.56 | 0.90 | 0.50 | 0.65 | -0.27 | -0.26 | -0.20 | -0.14 | -0.12 | -0.06 | 0.80 | -0.18 | -0.95 | -0.54 | -0.19 |
| Fe2O3 | -0.42 | 0.30 | 0.27 | 1.00 | 0.90 | 0.09 | 0.47 | -0.69 | 0.08 | 0.61 | 0.38 | 0.30 | 0.30 | 0.28 | 0.27 | 0.26 | 0.10 | 0.28 | -0.62 | -0.32 | 0.25 | -0.18 |
| FeO | -0.72 | 0.62 | 0.56 | 0.90 | 1.00 | 0.32 | 0.63 | -0.72 | 0.42 | 0.63 | 0.59 | 0.02 | 0.03 | 0.04 | 0.04 | 0.05 | 0.22 | 0.60 | -0.70 | -0.65 | -0.05 | -0.34 |
| MnO | -0.65 | 0.68 | 0.43 | 0.09 | 0.32 | 1.00 | 0.42 | 0.02 | 0.59 | 0.05 | 0.24 | -0.63 | -0.62 | -0.59 | -0.55 | -0.53 | 0.68 | 0.61 | -0.51 | -0.61 | -0.52 | -0.73 |
| MgO | -0.68 | 0.49 | 0.20 | 0.47 | 0.63 | 0.42 | 1.00 | -0.53 | 0.39 | 0.28 | 0.67 | -0.50 | -0.51 | -0.52 | -0.53 | -0.54 | 0.73 | 0.68 | -0.95 | -0.44 | -0.03 | -0.64 |
| CaO | 0.65 | -0.53 | -0.56 | -0.69 | -0.72 | 0.02 | -0.53 | 1.00 | -0.48 | -0.79 | -0.75 | 0.02 | 0.03 | 0.05 | 0.06 | 0.08 | -0.02 | -0.62 | 0.51 | 0.56 | -0.21 | 0.15 |
| Na2O | -0.90 | 0.95 | 0.90 | 0.08 | 0.42 | 0.59 | 0.39 | -0.48 | 1.00 | 0.33 | 0.65 | -0.66 | -0.65 | -0.61 | -0.55 | -0.54 | 0.29 | 0.93 | -0.33 | -0.93 | -0.60 | -0.48 |
| K2O | -0.51 | 0.45 | 0.50 | 0.61 | 0.63 | 0.05 | 0.28 | -0.79 | 0.33 | 1.00 | 0.37 | 0.16 | 0.16 | 0.12 | 0.07 | 0.07 | -0.11 | 0.44 | -0.25 | -0.50 | 0.43 | -0.11 |
| P2O5 | -0.73 | 0.61 | 0.65 | 0.38 | 0.59 | 0.24 | 0.67 | -0.75 | 0.65 | 0.37 | 1.00 | -0.30 | -0.30 | -0.26 | -0.23 | -0.22 | 0.16 | 0.77 | -0.60 | -0.71 | -0.32 | -0.15 |
| (CIW) | 0.59 | -0.58 | -0.27 | 0.30 | 0.02 | -0.63 | -0.50 | 0.02 | -0.66 | 0.16 | -0.30 | 1.00 | 1.00 | 0.99 | 0.97 | 0.97 | -0.77 | -0.67 | 0.42 | 0.42 | 0.43 | 0.76 |
| (CPA) | 0.58 | -0.57 | -0.26 | 0.30 | 0.03 | -0.62 | -0.51 | 0.03 | -0.65 | 0.16 | -0.30 | 1.00 | 1.00 | 0.99 | 0.97 | 0.97 | -0.77 | -0.67 | 0.43 | 0.41 | 0.42 | 0.76 |
| (CIA) | 0.56 | -0.54 | -0.20 | 0.28 | 0.04 | -0.59 | -0.52 | 0.05 | -0.61 | 0.12 | -0.26 | 0.99 | 0.99 | 1.00 | 0.99 | 0.99 | -0.79 | -0.64 | 0.44 | 0.37 | 0.32 | 0.78 |
| (PIA) | 0.53 | -0.49 | -0.14 | 0.27 | 0.04 | -0.55 | -0.53 | 0.06 | -0.55 | 0.07 | -0.23 | 0.97 | 0.97 | 0.99 | 1.00 | 1.00 | -0.81 | -0.61 | 0.45 | 0.31 | 0.21 | 0.79 |
| (CIX) | 0.52 | -0.47 | -0.12 | 0.26 | 0.05 | -0.53 | -0.54 | 0.08 | -0.54 | 0.07 | -0.22 | 0.97 | 0.97 | 0.99 | 1.00 | 1.00 | -0.81 | -0.60 | 0.46 | 0.29 | 0.19 | 0.79 |
| (ICV) | -0.48 | 0.39 | -0.06 | 0.10 | 0.22 | 0.68 | 0.73 | -0.02 | 0.29 | -0.11 | 0.16 | -0.77 | -0.77 | -0.79 | -0.81 | -0.81 | 1.00 | 0.48 | -0.75 | -0.19 | -0.12 | -0.92 |
| (WIP) | -0.98 | 0.94 | 0.80 | 0.28 | 0.60 | 0.61 | 0.68 | -0.62 | 0.93 | 0.44 | 0.77 | -0.67 | -0.67 | -0.64 | -0.61 | -0.60 | 0.48 | 1.00 | -0.62 | -0.91 | -0.43 | -0.61 |
| (W) | 0.66 | -0.47 | -0.18 | -0.62 | -0.70 | -0.51 | -0.95 | 0.51 | -0.33 | -0.25 | -0.60 | 0.42 | 0.43 | 0.44 | 0.45 | 0.46 | -0.75 | -0.62 | 1.00 | 0.41 | 0.04 | 0.66 |
| SiO2/Al2O3 | 0.93 | -0.95 | -0.95 | -0.32 | -0.65 | -0.61 | -0.44 | 0.56 | -0.93 | -0.50 | -0.71 | 0.42 | 0.41 | 0.37 | 0.31 | 0.29 | -0.19 | -0.91 | 0.41 | 1.00 | 0.54 | 0.39 |
| K2O/Al2O3 | 0.40 | -0.51 | -0.54 | 0.25 | -0.05 | -0.52 | -0.03 | -0.21 | -0.60 | 0.43 | -0.32 | 0.43 | 0.42 | 0.32 | 0.21 | 0.19 | -0.12 | -0.43 | 0.04 | 0.54 | 1.00 | 0.16 |
| Al2O3/TiO2 | 0.64 | -0.62 | -0.19 | -0.18 | -0.34 | -0.73 | -0.64 | 0.15 | -0.48 | -0.11 | -0.15 | 0.76 | 0.76 | 0.78 | 0.79 | 0.79 | -0.92 | -0.61 | 0.66 | 0.39 | 0.16 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('Barail_Group')
corrMatrix_bg.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.90 | -0.61 | -0.49 | -0.68 | -0.89 | -0.75 | -0.71 | -0.88 | 0.05 | -0.21 | 0.64 | 0.63 | 0.61 | 0.60 | 0.56 | -0.58 | -0.89 | 0.82 | 0.75 | 0.31 | 0.60 |
| TiO2 | -0.90 | 1.00 | 0.39 | 0.25 | 0.45 | 0.64 | 0.73 | 0.56 | 0.85 | 0.22 | 0.48 | -0.73 | -0.72 | -0.74 | -0.73 | -0.73 | 0.77 | 0.92 | -0.69 | -0.55 | 0.05 | -0.84 |
| Al2O3 | -0.61 | 0.39 | 1.00 | 0.56 | 0.68 | 0.71 | 0.15 | 0.26 | 0.29 | 0.12 | 0.26 | 0.13 | 0.15 | 0.16 | 0.17 | 0.22 | -0.26 | 0.30 | -0.27 | -0.97 | -0.37 | 0.16 |
| Fe2O3 | -0.49 | 0.25 | 0.56 | 1.00 | 0.96 | 0.51 | 0.33 | 0.34 | 0.14 | -0.20 | -0.03 | 0.16 | 0.14 | 0.20 | 0.22 | 0.23 | -0.03 | 0.14 | -0.50 | -0.57 | -0.41 | 0.11 |
| FeO | -0.68 | 0.45 | 0.68 | 0.96 | 1.00 | 0.69 | 0.50 | 0.39 | 0.36 | -0.21 | 0.00 | -0.02 | -0.03 | 0.03 | 0.05 | 0.08 | 0.09 | 0.35 | -0.65 | -0.70 | -0.48 | -0.04 |
| MnO | -0.89 | 0.64 | 0.71 | 0.51 | 0.69 | 1.00 | 0.64 | 0.64 | 0.82 | -0.28 | -0.10 | -0.51 | -0.50 | -0.45 | -0.43 | -0.37 | 0.31 | 0.75 | -0.81 | -0.78 | -0.56 | -0.29 |
| MgO | -0.75 | 0.73 | 0.15 | 0.33 | 0.50 | 0.64 | 1.00 | 0.39 | 0.81 | -0.25 | -0.07 | -0.74 | -0.75 | -0.71 | -0.70 | -0.68 | 0.76 | 0.83 | -0.93 | -0.25 | -0.23 | -0.66 |
| CaO | -0.71 | 0.56 | 0.26 | 0.34 | 0.39 | 0.64 | 0.39 | 1.00 | 0.67 | -0.11 | 0.04 | -0.57 | -0.57 | -0.54 | -0.53 | -0.52 | 0.51 | 0.62 | -0.55 | -0.46 | -0.21 | -0.46 |
| Na2O | -0.88 | 0.85 | 0.29 | 0.14 | 0.36 | 0.82 | 0.81 | 0.67 | 1.00 | -0.17 | 0.03 | -0.91 | -0.90 | -0.88 | -0.87 | -0.83 | 0.76 | 0.97 | -0.85 | -0.44 | -0.25 | -0.75 |
| K2O | 0.05 | 0.22 | 0.12 | -0.20 | -0.21 | -0.28 | -0.25 | -0.11 | -0.17 | 1.00 | 0.95 | 0.14 | 0.15 | 0.03 | 0.01 | -0.06 | 0.03 | 0.05 | 0.44 | -0.14 | 0.87 | -0.19 |
| P2O5 | -0.21 | 0.48 | 0.26 | -0.03 | 0.00 | -0.10 | -0.07 | 0.04 | 0.03 | 0.95 | 1.00 | 0.01 | 0.01 | -0.09 | -0.11 | -0.17 | 0.19 | 0.24 | 0.23 | -0.32 | 0.76 | -0.37 |
| (CIW) | 0.64 | -0.73 | 0.13 | 0.16 | -0.02 | -0.51 | -0.74 | -0.57 | -0.91 | 0.14 | 0.01 | 1.00 | 1.00 | 0.99 | 0.99 | 0.97 | -0.91 | -0.88 | 0.72 | 0.04 | 0.03 | 0.87 |
| (CPA) | 0.63 | -0.72 | 0.15 | 0.14 | -0.03 | -0.50 | -0.75 | -0.57 | -0.90 | 0.15 | 0.01 | 1.00 | 1.00 | 0.99 | 0.99 | 0.97 | -0.92 | -0.88 | 0.73 | 0.02 | 0.02 | 0.88 |
| (CIA) | 0.61 | -0.74 | 0.16 | 0.20 | 0.03 | -0.45 | -0.71 | -0.54 | -0.88 | 0.03 | -0.09 | 0.99 | 0.99 | 1.00 | 1.00 | 0.99 | -0.93 | -0.88 | 0.66 | 0.01 | -0.09 | 0.90 |
| (PIA) | 0.60 | -0.73 | 0.17 | 0.22 | 0.05 | -0.43 | -0.70 | -0.53 | -0.87 | 0.01 | -0.11 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | -0.92 | -0.87 | 0.64 | 0.00 | -0.12 | 0.90 |
| (CIX) | 0.56 | -0.73 | 0.22 | 0.23 | 0.08 | -0.37 | -0.68 | -0.52 | -0.83 | -0.06 | -0.17 | 0.97 | 0.97 | 0.99 | 1.00 | 1.00 | -0.94 | -0.85 | 0.60 | -0.04 | -0.21 | 0.93 |
| (ICV) | -0.58 | 0.77 | -0.26 | -0.03 | 0.09 | 0.31 | 0.76 | 0.51 | 0.76 | 0.03 | 0.19 | -0.91 | -0.92 | -0.93 | -0.92 | -0.94 | 1.00 | 0.81 | -0.66 | 0.07 | 0.20 | -0.96 |
| (WIP) | -0.89 | 0.92 | 0.30 | 0.14 | 0.35 | 0.75 | 0.83 | 0.62 | 0.97 | 0.05 | 0.24 | -0.88 | -0.88 | -0.88 | -0.87 | -0.85 | 0.81 | 1.00 | -0.80 | -0.46 | -0.05 | -0.81 |
| (W) | 0.82 | -0.69 | -0.27 | -0.50 | -0.65 | -0.81 | -0.93 | -0.55 | -0.85 | 0.44 | 0.23 | 0.72 | 0.73 | 0.66 | 0.64 | 0.60 | -0.66 | -0.80 | 1.00 | 0.37 | 0.47 | 0.55 |
| SiO2/Al2O3 | 0.75 | -0.55 | -0.97 | -0.57 | -0.70 | -0.78 | -0.25 | -0.46 | -0.44 | -0.14 | -0.32 | 0.04 | 0.02 | 0.01 | 0.00 | -0.04 | 0.07 | -0.46 | 0.37 | 1.00 | 0.33 | 0.03 |
| K2O/Al2O3 | 0.31 | 0.05 | -0.37 | -0.41 | -0.48 | -0.56 | -0.23 | -0.21 | -0.25 | 0.87 | 0.76 | 0.03 | 0.02 | -0.09 | -0.12 | -0.21 | 0.20 | -0.05 | 0.47 | 0.33 | 1.00 | -0.28 |
| Al2O3/TiO2 | 0.60 | -0.84 | 0.16 | 0.11 | -0.04 | -0.29 | -0.66 | -0.46 | -0.75 | -0.19 | -0.37 | 0.87 | 0.88 | 0.90 | 0.90 | 0.93 | -0.96 | -0.81 | 0.55 | 0.03 | -0.28 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('channel_sediments')
corrMatrix_cs.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | 0.12 | -0.35 | -0.26 | -0.04 | -0.75 | -0.25 | -0.91 | -0.20 | -0.60 | -0.10 | -0.05 | -0.05 | 0.42 | 0.45 | 0.49 | -0.85 | -0.53 | 0.64 | 0.55 | -0.54 | -0.79 |
| TiO2 | 0.12 | 1.00 | 0.22 | 0.70 | 0.78 | 0.14 | 0.50 | -0.22 | 0.19 | -0.19 | 0.50 | -0.17 | -0.15 | 0.14 | 0.17 | 0.25 | -0.21 | -0.01 | 0.24 | -0.25 | -0.36 | -0.55 |
| Al2O3 | -0.35 | 0.22 | 1.00 | 0.11 | 0.39 | 0.21 | 0.55 | -0.03 | 0.92 | 0.58 | 0.53 | -0.48 | -0.47 | -0.41 | -0.40 | -0.27 | -0.16 | 0.86 | -0.16 | -0.84 | 0.05 | -0.04 |
| Fe2O3 | -0.26 | 0.70 | 0.11 | 1.00 | 0.89 | 0.67 | 0.56 | 0.18 | 0.10 | -0.19 | 0.60 | -0.18 | -0.16 | 0.05 | 0.05 | 0.16 | 0.20 | -0.04 | 0.01 | -0.31 | -0.25 | -0.18 |
| FeO | -0.04 | 0.78 | 0.39 | 0.89 | 1.00 | 0.47 | 0.72 | -0.16 | 0.38 | -0.22 | 0.82 | -0.33 | -0.31 | 0.14 | 0.15 | 0.33 | -0.19 | 0.07 | 0.21 | -0.41 | -0.49 | -0.46 |
| MnO | -0.75 | 0.14 | 0.21 | 0.67 | 0.47 | 1.00 | 0.44 | 0.69 | 0.11 | 0.24 | 0.52 | -0.02 | -0.01 | -0.22 | -0.25 | -0.22 | 0.66 | 0.25 | -0.34 | -0.45 | 0.23 | 0.51 |
| MgO | -0.25 | 0.50 | 0.55 | 0.56 | 0.72 | 0.44 | 1.00 | 0.07 | 0.49 | -0.17 | 0.77 | -0.31 | -0.30 | 0.14 | 0.13 | 0.32 | -0.00 | 0.18 | 0.01 | -0.56 | -0.47 | -0.13 |
| CaO | -0.91 | -0.22 | -0.03 | 0.18 | -0.16 | 0.69 | 0.07 | 1.00 | -0.16 | 0.41 | -0.13 | 0.25 | 0.25 | -0.29 | -0.33 | -0.42 | 0.99 | 0.22 | -0.62 | -0.24 | 0.57 | 0.89 |
| Na2O | -0.20 | 0.19 | 0.92 | 0.10 | 0.38 | 0.11 | 0.49 | -0.16 | 1.00 | 0.45 | 0.46 | -0.78 | -0.76 | -0.52 | -0.52 | -0.29 | -0.27 | 0.81 | -0.28 | -0.75 | -0.07 | -0.14 |
| K2O | -0.60 | -0.19 | 0.58 | -0.19 | -0.22 | 0.24 | -0.17 | 0.41 | 0.45 | 1.00 | -0.16 | -0.10 | -0.09 | -0.74 | -0.74 | -0.84 | 0.34 | 0.89 | -0.44 | -0.60 | 0.82 | 0.49 |
| P2O5 | -0.10 | 0.50 | 0.53 | 0.60 | 0.82 | 0.52 | 0.77 | -0.13 | 0.46 | -0.16 | 1.00 | -0.24 | -0.23 | 0.21 | 0.20 | 0.37 | -0.21 | 0.16 | 0.29 | -0.44 | -0.48 | -0.29 |
| (CIW) | -0.05 | -0.17 | -0.48 | -0.18 | -0.33 | -0.02 | -0.31 | 0.25 | -0.78 | -0.10 | -0.24 | 1.00 | 1.00 | 0.58 | 0.59 | 0.28 | 0.29 | -0.45 | 0.39 | 0.49 | 0.19 | 0.23 |
| (CPA) | -0.05 | -0.15 | -0.47 | -0.16 | -0.31 | -0.01 | -0.30 | 0.25 | -0.76 | -0.09 | -0.23 | 1.00 | 1.00 | 0.58 | 0.60 | 0.28 | 0.29 | -0.44 | 0.40 | 0.48 | 0.18 | 0.23 |
| (CIA) | 0.42 | 0.14 | -0.41 | 0.05 | 0.14 | -0.22 | 0.14 | -0.29 | -0.52 | -0.74 | 0.21 | 0.58 | 0.58 | 1.00 | 1.00 | 0.94 | -0.27 | -0.74 | 0.65 | 0.62 | -0.69 | -0.38 |
| (PIA) | 0.45 | 0.17 | -0.40 | 0.05 | 0.15 | -0.25 | 0.13 | -0.33 | -0.52 | -0.74 | 0.20 | 0.59 | 0.60 | 1.00 | 1.00 | 0.93 | -0.30 | -0.74 | 0.69 | 0.60 | -0.68 | -0.42 |
| (CIX) | 0.49 | 0.25 | -0.27 | 0.16 | 0.33 | -0.22 | 0.32 | -0.42 | -0.29 | -0.84 | 0.37 | 0.28 | 0.28 | 0.94 | 0.93 | 1.00 | -0.41 | -0.69 | 0.60 | 0.51 | -0.89 | -0.53 |
| (ICV) | -0.85 | -0.21 | -0.16 | 0.20 | -0.19 | 0.66 | -0.00 | 0.99 | -0.27 | 0.34 | -0.21 | 0.29 | 0.29 | -0.27 | -0.30 | -0.41 | 1.00 | 0.11 | -0.59 | -0.14 | 0.57 | 0.87 |
| (WIP) | -0.53 | -0.01 | 0.86 | -0.04 | 0.07 | 0.25 | 0.18 | 0.22 | 0.81 | 0.89 | 0.16 | -0.45 | -0.44 | -0.74 | -0.74 | -0.69 | 0.11 | 1.00 | -0.43 | -0.80 | 0.50 | 0.27 |
| (W) | 0.64 | 0.24 | -0.16 | 0.01 | 0.21 | -0.34 | 0.01 | -0.62 | -0.28 | -0.44 | 0.29 | 0.39 | 0.40 | 0.65 | 0.69 | 0.60 | -0.59 | -0.43 | 1.00 | 0.37 | -0.44 | -0.63 |
| SiO2/Al2O3 | 0.55 | -0.25 | -0.84 | -0.31 | -0.41 | -0.45 | -0.56 | -0.24 | -0.75 | -0.60 | -0.44 | 0.49 | 0.48 | 0.62 | 0.60 | 0.51 | -0.14 | -0.80 | 0.37 | 1.00 | -0.29 | -0.18 |
| K2O/Al2O3 | -0.54 | -0.36 | 0.05 | -0.25 | -0.49 | 0.23 | -0.47 | 0.57 | -0.07 | 0.82 | -0.48 | 0.19 | 0.18 | -0.69 | -0.68 | -0.89 | 0.57 | 0.50 | -0.44 | -0.29 | 1.00 | 0.68 |
| Al2O3/TiO2 | -0.79 | -0.55 | -0.04 | -0.18 | -0.46 | 0.51 | -0.13 | 0.89 | -0.14 | 0.49 | -0.29 | 0.23 | 0.23 | -0.38 | -0.42 | -0.53 | 0.87 | 0.27 | -0.63 | -0.18 | 0.68 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('overbank_sediments')
corrMatrix_os.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.50 | -0.75 | -0.19 | -0.75 | -0.32 | -0.65 | -0.31 | 0.74 | 0.03 | -0.41 | -0.83 | -0.83 | -0.80 | -0.81 | -0.76 | 0.21 | 0.23 | 0.15 | 0.90 | 0.55 | 0.30 |
| TiO2 | -0.50 | 1.00 | 0.67 | 0.33 | 0.82 | 0.67 | 0.16 | -0.40 | -0.73 | -0.36 | 0.35 | 0.78 | 0.77 | 0.83 | 0.82 | 0.84 | -0.68 | -0.58 | 0.51 | -0.66 | -0.77 | -0.65 |
| Al2O3 | -0.75 | 0.67 | 1.00 | -0.13 | 0.92 | 0.62 | 0.43 | -0.36 | -0.60 | 0.22 | 0.42 | 0.81 | 0.80 | 0.77 | 0.80 | 0.72 | -0.76 | -0.04 | 0.43 | -0.94 | -0.49 | -0.31 |
| Fe2O3 | -0.19 | 0.33 | -0.13 | 1.00 | 0.20 | 0.17 | 0.24 | 0.31 | -0.30 | -0.53 | 0.22 | 0.19 | 0.21 | 0.26 | 0.27 | 0.31 | 0.24 | -0.46 | -0.04 | -0.03 | -0.45 | -0.59 |
| FeO | -0.75 | 0.82 | 0.92 | 0.20 | 1.00 | 0.69 | 0.38 | -0.31 | -0.73 | -0.01 | 0.43 | 0.86 | 0.85 | 0.85 | 0.87 | 0.82 | -0.69 | -0.29 | 0.41 | -0.87 | -0.64 | -0.47 |
| MnO | -0.32 | 0.67 | 0.62 | 0.17 | 0.69 | 1.00 | 0.06 | -0.47 | -0.44 | 0.05 | 0.40 | 0.55 | 0.54 | 0.53 | 0.56 | 0.51 | -0.63 | -0.18 | 0.53 | -0.54 | -0.40 | -0.35 |
| MgO | -0.65 | 0.16 | 0.43 | 0.24 | 0.38 | 0.06 | 1.00 | 0.29 | -0.19 | 0.02 | 0.64 | 0.34 | 0.35 | 0.35 | 0.38 | 0.37 | 0.06 | 0.18 | -0.12 | -0.56 | -0.38 | -0.39 |
| CaO | -0.31 | -0.40 | -0.36 | 0.31 | -0.31 | -0.47 | 0.29 | 1.00 | -0.03 | -0.20 | -0.08 | -0.11 | -0.09 | -0.10 | -0.12 | -0.08 | 0.83 | -0.08 | -0.86 | 0.10 | 0.07 | 0.17 |
| Na2O | 0.74 | -0.73 | -0.60 | -0.30 | -0.73 | -0.44 | -0.19 | -0.03 | 1.00 | 0.31 | -0.11 | -0.95 | -0.95 | -0.93 | -0.93 | -0.90 | 0.40 | 0.68 | -0.04 | 0.72 | 0.64 | 0.36 |
| K2O | 0.03 | -0.36 | 0.22 | -0.53 | -0.01 | 0.05 | 0.02 | -0.20 | 0.31 | 1.00 | 0.01 | -0.15 | -0.15 | -0.34 | -0.28 | -0.45 | -0.10 | 0.86 | -0.02 | -0.10 | 0.72 | 0.66 |
| P2O5 | -0.41 | 0.35 | 0.42 | 0.22 | 0.43 | 0.40 | 0.64 | -0.08 | -0.11 | 0.01 | 1.00 | 0.27 | 0.28 | 0.30 | 0.32 | 0.33 | -0.18 | 0.12 | 0.22 | -0.45 | -0.39 | -0.49 |
| (CIW) | -0.83 | 0.78 | 0.81 | 0.19 | 0.86 | 0.55 | 0.34 | -0.11 | -0.95 | -0.15 | 0.27 | 1.00 | 1.00 | 0.98 | 0.99 | 0.94 | -0.57 | -0.51 | 0.20 | -0.89 | -0.68 | -0.41 |
| (CPA) | -0.83 | 0.77 | 0.80 | 0.21 | 0.85 | 0.54 | 0.35 | -0.09 | -0.95 | -0.15 | 0.28 | 1.00 | 1.00 | 0.97 | 0.98 | 0.93 | -0.56 | -0.50 | 0.19 | -0.89 | -0.67 | -0.42 |
| (CIA) | -0.80 | 0.83 | 0.77 | 0.26 | 0.85 | 0.53 | 0.35 | -0.10 | -0.93 | -0.34 | 0.30 | 0.98 | 0.97 | 1.00 | 0.99 | 0.99 | -0.57 | -0.63 | 0.24 | -0.86 | -0.82 | -0.55 |
| (PIA) | -0.81 | 0.82 | 0.80 | 0.27 | 0.87 | 0.56 | 0.38 | -0.12 | -0.93 | -0.28 | 0.32 | 0.99 | 0.98 | 0.99 | 1.00 | 0.98 | -0.58 | -0.57 | 0.26 | -0.88 | -0.79 | -0.55 |
| (CIX) | -0.76 | 0.84 | 0.72 | 0.31 | 0.82 | 0.51 | 0.37 | -0.08 | -0.90 | -0.45 | 0.33 | 0.94 | 0.93 | 0.99 | 0.98 | 1.00 | -0.55 | -0.68 | 0.26 | -0.82 | -0.88 | -0.64 |
| (ICV) | 0.21 | -0.68 | -0.76 | 0.24 | -0.69 | -0.63 | 0.06 | 0.83 | 0.40 | -0.10 | -0.18 | -0.57 | -0.56 | -0.57 | -0.58 | -0.55 | 1.00 | 0.16 | -0.80 | 0.58 | 0.42 | 0.34 |
| (WIP) | 0.23 | -0.58 | -0.04 | -0.46 | -0.29 | -0.18 | 0.18 | -0.08 | 0.68 | 0.86 | 0.12 | -0.51 | -0.50 | -0.63 | -0.57 | -0.68 | 0.16 | 1.00 | -0.07 | 0.16 | 0.73 | 0.54 |
| (W) | 0.15 | 0.51 | 0.43 | -0.04 | 0.41 | 0.53 | -0.12 | -0.86 | -0.04 | -0.02 | 0.22 | 0.20 | 0.19 | 0.24 | 0.26 | 0.26 | -0.80 | -0.07 | 1.00 | -0.24 | -0.35 | -0.54 |
| SiO2/Al2O3 | 0.90 | -0.66 | -0.94 | -0.03 | -0.87 | -0.54 | -0.56 | 0.10 | 0.72 | -0.10 | -0.45 | -0.89 | -0.89 | -0.86 | -0.88 | -0.82 | 0.58 | 0.16 | -0.24 | 1.00 | 0.58 | 0.37 |
| K2O/Al2O3 | 0.55 | -0.77 | -0.49 | -0.45 | -0.64 | -0.40 | -0.38 | 0.07 | 0.64 | 0.72 | -0.39 | -0.68 | -0.67 | -0.82 | -0.79 | -0.88 | 0.42 | 0.73 | -0.35 | 0.58 | 1.00 | 0.87 |
| Al2O3/TiO2 | 0.30 | -0.65 | -0.31 | -0.59 | -0.47 | -0.35 | -0.39 | 0.17 | 0.36 | 0.66 | -0.49 | -0.41 | -0.42 | -0.55 | -0.55 | -0.64 | 0.34 | 0.54 | -0.54 | 0.37 | 0.87 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('suspended_sediments')
corrMatrix_ss.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.63 | -0.03 | -0.16 | -0.63 | 0.15 | 0.19 | -0.15 | 0.71 | 0.57 | 0.13 | -0.59 | -0.60 | -0.60 | -0.62 | -0.59 | -0.02 | 0.62 | 0.21 | 0.36 | 0.58 | 0.48 |
| TiO2 | -0.63 | 1.00 | -0.13 | 0.22 | 0.47 | -0.49 | 0.08 | 0.08 | -0.45 | -0.58 | -0.46 | 0.34 | 0.35 | 0.45 | 0.38 | 0.49 | 0.03 | -0.49 | -0.32 | -0.09 | -0.56 | -0.82 |
| Al2O3 | -0.03 | -0.13 | 1.00 | -0.92 | 0.63 | 0.30 | -0.53 | -0.78 | -0.30 | 0.24 | 0.36 | 0.58 | 0.57 | 0.45 | 0.52 | 0.37 | -0.91 | -0.04 | 0.91 | -0.93 | -0.14 | 0.54 |
| Fe2O3 | -0.16 | 0.22 | -0.92 | 1.00 | -0.37 | -0.17 | 0.43 | 0.73 | 0.11 | -0.32 | -0.33 | -0.38 | -0.36 | -0.28 | -0.32 | -0.21 | 0.83 | -0.09 | -0.89 | 0.77 | 0.02 | -0.50 |
| FeO | -0.63 | 0.47 | 0.63 | -0.37 | 1.00 | 0.28 | -0.66 | -0.55 | -0.77 | -0.41 | 0.13 | 0.85 | 0.85 | 0.84 | 0.85 | 0.80 | -0.70 | -0.62 | 0.47 | -0.79 | -0.67 | 0.05 |
| MnO | 0.15 | -0.49 | 0.30 | -0.17 | 0.28 | 1.00 | -0.76 | -0.29 | -0.36 | -0.20 | 0.18 | 0.41 | 0.41 | 0.41 | 0.41 | 0.39 | -0.39 | -0.39 | 0.36 | -0.23 | -0.31 | 0.80 |
| MgO | 0.19 | 0.08 | -0.53 | 0.43 | -0.66 | -0.76 | 1.00 | 0.49 | 0.59 | 0.43 | -0.19 | -0.65 | -0.64 | -0.71 | -0.66 | -0.70 | 0.65 | 0.66 | -0.53 | 0.50 | 0.63 | -0.53 |
| CaO | -0.15 | 0.08 | -0.78 | 0.73 | -0.55 | -0.29 | 0.49 | 1.00 | 0.05 | -0.34 | -0.50 | -0.31 | -0.30 | -0.22 | -0.26 | -0.15 | 0.94 | -0.10 | -0.92 | 0.67 | -0.02 | -0.48 |
| Na2O | 0.71 | -0.45 | -0.30 | 0.11 | -0.77 | -0.36 | 0.59 | 0.05 | 1.00 | 0.72 | 0.20 | -0.95 | -0.95 | -0.95 | -0.97 | -0.93 | 0.31 | 0.88 | -0.05 | 0.53 | 0.85 | 0.07 |
| K2O | 0.57 | -0.58 | 0.24 | -0.32 | -0.41 | -0.20 | 0.43 | -0.34 | 0.72 | 1.00 | 0.49 | -0.51 | -0.52 | -0.72 | -0.59 | -0.80 | -0.15 | 0.94 | 0.42 | -0.07 | 0.92 | 0.37 |
| P2O5 | 0.13 | -0.46 | 0.36 | -0.33 | 0.13 | 0.18 | -0.19 | -0.50 | 0.20 | 0.49 | 1.00 | -0.06 | -0.06 | -0.20 | -0.10 | -0.26 | -0.39 | 0.34 | 0.57 | -0.29 | 0.37 | 0.47 |
| (CIW) | -0.59 | 0.34 | 0.58 | -0.38 | 0.85 | 0.41 | -0.65 | -0.31 | -0.95 | -0.51 | -0.06 | 1.00 | 1.00 | 0.95 | 1.00 | 0.90 | -0.57 | -0.74 | 0.34 | -0.76 | -0.75 | 0.13 |
| (CPA) | -0.60 | 0.35 | 0.57 | -0.36 | 0.85 | 0.41 | -0.64 | -0.30 | -0.95 | -0.52 | -0.06 | 1.00 | 1.00 | 0.95 | 1.00 | 0.90 | -0.56 | -0.75 | 0.33 | -0.75 | -0.75 | 0.12 |
| (CIA) | -0.60 | 0.45 | 0.45 | -0.28 | 0.84 | 0.41 | -0.71 | -0.22 | -0.95 | -0.72 | -0.20 | 0.95 | 0.95 | 1.00 | 0.97 | 0.99 | -0.48 | -0.89 | 0.21 | -0.61 | -0.91 | 0.03 |
| (PIA) | -0.62 | 0.38 | 0.52 | -0.32 | 0.85 | 0.41 | -0.66 | -0.26 | -0.97 | -0.59 | -0.10 | 1.00 | 1.00 | 0.97 | 1.00 | 0.94 | -0.52 | -0.80 | 0.28 | -0.70 | -0.81 | 0.08 |
| (CIX) | -0.59 | 0.49 | 0.37 | -0.21 | 0.80 | 0.39 | -0.70 | -0.15 | -0.93 | -0.80 | -0.26 | 0.90 | 0.90 | 0.99 | 0.94 | 1.00 | -0.41 | -0.93 | 0.13 | -0.52 | -0.96 | -0.03 |
| (ICV) | -0.02 | 0.03 | -0.91 | 0.83 | -0.70 | -0.39 | 0.65 | 0.94 | 0.31 | -0.15 | -0.39 | -0.57 | -0.56 | -0.48 | -0.52 | -0.41 | 1.00 | 0.13 | -0.93 | 0.83 | 0.22 | -0.51 |
| (WIP) | 0.62 | -0.49 | -0.04 | -0.09 | -0.62 | -0.39 | 0.66 | -0.10 | 0.88 | 0.94 | 0.34 | -0.74 | -0.75 | -0.89 | -0.80 | -0.93 | 0.13 | 1.00 | 0.15 | 0.22 | 0.97 | 0.15 |
| (W) | 0.21 | -0.32 | 0.91 | -0.89 | 0.47 | 0.36 | -0.53 | -0.92 | -0.05 | 0.42 | 0.57 | 0.34 | 0.33 | 0.21 | 0.28 | 0.13 | -0.93 | 0.15 | 1.00 | -0.76 | 0.07 | 0.67 |
| SiO2/Al2O3 | 0.36 | -0.09 | -0.93 | 0.77 | -0.79 | -0.23 | 0.50 | 0.67 | 0.53 | -0.07 | -0.29 | -0.76 | -0.75 | -0.61 | -0.70 | -0.52 | 0.83 | 0.22 | -0.76 | 1.00 | 0.29 | -0.34 |
| K2O/Al2O3 | 0.58 | -0.56 | -0.14 | 0.02 | -0.67 | -0.31 | 0.63 | -0.02 | 0.85 | 0.92 | 0.37 | -0.75 | -0.75 | -0.91 | -0.81 | -0.96 | 0.22 | 0.97 | 0.07 | 0.29 | 1.00 | 0.18 |
| Al2O3/TiO2 | 0.48 | -0.82 | 0.54 | -0.50 | 0.05 | 0.80 | -0.53 | -0.48 | 0.07 | 0.37 | 0.47 | 0.13 | 0.12 | 0.03 | 0.08 | -0.03 | -0.51 | 0.15 | 0.67 | -0.34 | 0.18 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('Silty_clay')
corrMatrix_sc.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.46 | -0.92 | -0.14 | -0.92 | -0.23 | -0.85 | -0.71 | 0.56 | -0.86 | -0.32 | -0.81 | -0.81 | -0.70 | -0.79 | -0.48 | -0.30 | -0.73 | 0.63 | 0.97 | -0.59 | -0.53 |
| TiO2 | -0.46 | 1.00 | 0.51 | 0.35 | 0.61 | -0.22 | 0.66 | -0.00 | 0.31 | 0.41 | -0.14 | 0.01 | 0.01 | -0.09 | -0.02 | -0.19 | 0.25 | 0.72 | -0.36 | -0.53 | 0.19 | -0.43 |
| Al2O3 | -0.92 | 0.51 | 1.00 | -0.13 | 0.85 | 0.04 | 0.77 | 0.51 | -0.50 | 0.83 | 0.19 | 0.80 | 0.80 | 0.74 | 0.79 | 0.56 | 0.02 | 0.71 | -0.35 | -0.98 | 0.48 | 0.56 |
| Fe2O3 | -0.14 | 0.35 | -0.13 | 1.00 | 0.39 | 0.40 | 0.29 | 0.12 | 0.40 | -0.06 | 0.39 | -0.34 | -0.34 | -0.40 | -0.36 | -0.42 | 0.62 | 0.24 | -0.67 | -0.01 | -0.04 | -0.48 |
| FeO | -0.92 | 0.61 | 0.85 | 0.39 | 1.00 | 0.34 | 0.79 | 0.48 | -0.29 | 0.66 | 0.48 | 0.59 | 0.59 | 0.54 | 0.58 | 0.41 | 0.24 | 0.68 | -0.61 | -0.91 | 0.34 | 0.31 |
| MnO | -0.23 | -0.22 | 0.04 | 0.40 | 0.34 | 1.00 | -0.13 | 0.17 | -0.20 | -0.18 | 0.86 | 0.17 | 0.18 | 0.34 | 0.22 | 0.48 | -0.03 | -0.30 | -0.23 | -0.14 | -0.31 | 0.25 |
| MgO | -0.85 | 0.66 | 0.77 | 0.29 | 0.79 | -0.13 | 1.00 | 0.70 | -0.28 | 0.91 | -0.07 | 0.54 | 0.53 | 0.30 | 0.48 | -0.02 | 0.62 | 0.97 | -0.77 | -0.82 | 0.77 | 0.18 |
| CaO | -0.71 | -0.00 | 0.51 | 0.12 | 0.48 | 0.17 | 0.70 | 1.00 | -0.68 | 0.81 | 0.10 | 0.70 | 0.70 | 0.47 | 0.66 | 0.15 | 0.67 | 0.58 | -0.79 | -0.59 | 0.81 | 0.54 |
| Na2O | 0.56 | 0.31 | -0.50 | 0.40 | -0.29 | -0.20 | -0.28 | -0.68 | 1.00 | -0.59 | -0.10 | -0.92 | -0.92 | -0.87 | -0.92 | -0.68 | -0.03 | -0.11 | 0.16 | 0.50 | -0.59 | -0.84 |
| K2O | -0.86 | 0.41 | 0.83 | -0.06 | 0.66 | -0.18 | 0.91 | 0.81 | -0.59 | 1.00 | -0.15 | 0.78 | 0.78 | 0.55 | 0.73 | 0.20 | 0.47 | 0.86 | -0.61 | -0.84 | 0.88 | 0.48 |
| P2O5 | -0.32 | -0.14 | 0.19 | 0.39 | 0.48 | 0.86 | -0.07 | 0.10 | -0.10 | -0.15 | 1.00 | 0.16 | 0.17 | 0.36 | 0.22 | 0.53 | -0.17 | -0.20 | -0.21 | -0.27 | -0.41 | 0.34 |
| (CIW) | -0.81 | 0.01 | 0.80 | -0.34 | 0.59 | 0.17 | 0.54 | 0.70 | -0.92 | 0.78 | 0.16 | 1.00 | 1.00 | 0.94 | 1.00 | 0.73 | 0.02 | 0.39 | -0.27 | -0.79 | 0.62 | 0.84 |
| (CPA) | -0.81 | 0.01 | 0.80 | -0.34 | 0.59 | 0.18 | 0.53 | 0.70 | -0.92 | 0.78 | 0.17 | 1.00 | 1.00 | 0.94 | 1.00 | 0.74 | 0.01 | 0.38 | -0.27 | -0.79 | 0.61 | 0.84 |
| (CIA) | -0.70 | -0.09 | 0.74 | -0.40 | 0.54 | 0.34 | 0.30 | 0.47 | -0.87 | 0.55 | 0.36 | 0.94 | 0.94 | 1.00 | 0.96 | 0.92 | -0.28 | 0.14 | -0.04 | -0.71 | 0.32 | 0.86 |
| (PIA) | -0.79 | -0.02 | 0.79 | -0.36 | 0.58 | 0.22 | 0.48 | 0.66 | -0.92 | 0.73 | 0.22 | 1.00 | 1.00 | 0.96 | 1.00 | 0.78 | -0.05 | 0.33 | -0.22 | -0.78 | 0.56 | 0.86 |
| (CIX) | -0.48 | -0.19 | 0.56 | -0.42 | 0.41 | 0.48 | -0.02 | 0.15 | -0.68 | 0.20 | 0.53 | 0.73 | 0.74 | 0.92 | 0.78 | 1.00 | -0.57 | -0.17 | 0.23 | -0.52 | -0.07 | 0.77 |
| (ICV) | -0.30 | 0.25 | 0.02 | 0.62 | 0.24 | -0.03 | 0.62 | 0.67 | -0.03 | 0.47 | -0.17 | 0.02 | 0.01 | -0.28 | -0.05 | -0.57 | 1.00 | 0.59 | -0.89 | -0.14 | 0.66 | -0.22 |
| (WIP) | -0.73 | 0.72 | 0.71 | 0.24 | 0.68 | -0.30 | 0.97 | 0.58 | -0.11 | 0.86 | -0.20 | 0.39 | 0.38 | 0.14 | 0.33 | -0.17 | 0.59 | 1.00 | -0.69 | -0.74 | 0.71 | 0.05 |
| (W) | 0.63 | -0.36 | -0.35 | -0.67 | -0.61 | -0.23 | -0.77 | -0.79 | 0.16 | -0.61 | -0.21 | -0.27 | -0.27 | -0.04 | -0.22 | 0.23 | -0.89 | -0.69 | 1.00 | 0.49 | -0.60 | -0.03 |
| SiO2/Al2O3 | 0.97 | -0.53 | -0.98 | -0.01 | -0.91 | -0.14 | -0.82 | -0.59 | 0.50 | -0.84 | -0.27 | -0.79 | -0.79 | -0.71 | -0.78 | -0.52 | -0.14 | -0.74 | 0.49 | 1.00 | -0.51 | -0.53 |
| K2O/Al2O3 | -0.59 | 0.19 | 0.48 | -0.04 | 0.34 | -0.31 | 0.77 | 0.81 | -0.59 | 0.88 | -0.41 | 0.62 | 0.61 | 0.32 | 0.56 | -0.07 | 0.66 | 0.71 | -0.60 | -0.51 | 1.00 | 0.33 |
| Al2O3/TiO2 | -0.53 | -0.43 | 0.56 | -0.48 | 0.31 | 0.25 | 0.18 | 0.54 | -0.84 | 0.48 | 0.34 | 0.84 | 0.84 | 0.86 | 0.86 | 0.77 | -0.22 | 0.05 | -0.03 | -0.53 | 0.33 | 1.00 |
# Visual representation of correlation matrix of subsubcategory ('Sand')
corrMatrix_s.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)
| SiO2 | TiO2 | Al2O3 | Fe2O3 | FeO | MnO | MgO | CaO | Na2O | K2O | P2O5 | (CIW) | (CPA) | (CIA) | (PIA) | (CIX) | (ICV) | (WIP) | (W) | SiO2/Al2O3 | K2O/Al2O3 | Al2O3/TiO2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SiO2 | 1.00 | -0.78 | -0.94 | -0.82 | -0.95 | -0.86 | -0.92 | -0.58 | -0.45 | -0.62 | -0.70 | -0.46 | -0.46 | -0.57 | -0.53 | -0.65 | -0.18 | -0.71 | 0.65 | 0.96 | 0.63 | 0.51 |
| TiO2 | -0.78 | 1.00 | 0.54 | 0.99 | 0.91 | 0.76 | 0.82 | 0.85 | 0.18 | 0.01 | 0.98 | 0.35 | 0.35 | 0.50 | 0.44 | 0.61 | 0.64 | 0.28 | -0.91 | -0.58 | -0.91 | -0.92 |
| Al2O3 | -0.94 | 0.54 | 1.00 | 0.59 | 0.81 | 0.79 | 0.81 | 0.32 | 0.49 | 0.85 | 0.43 | 0.48 | 0.48 | 0.54 | 0.52 | 0.58 | -0.13 | 0.80 | -0.39 | -1.00 | -0.42 | -0.21 |
| Fe2O3 | -0.82 | 0.99 | 0.59 | 1.00 | 0.95 | 0.79 | 0.84 | 0.80 | 0.15 | 0.08 | 0.95 | 0.44 | 0.44 | 0.58 | 0.53 | 0.69 | 0.58 | 0.29 | -0.87 | -0.64 | -0.88 | -0.91 |
| FeO | -0.95 | 0.91 | 0.81 | 0.95 | 1.00 | 0.90 | 0.88 | 0.66 | 0.23 | 0.38 | 0.85 | 0.56 | 0.56 | 0.68 | 0.64 | 0.77 | 0.32 | 0.47 | -0.74 | -0.84 | -0.80 | -0.74 |
| MnO | -0.86 | 0.76 | 0.79 | 0.79 | 0.90 | 1.00 | 0.65 | 0.38 | 0.08 | 0.46 | 0.72 | 0.68 | 0.68 | 0.76 | 0.74 | 0.81 | 0.04 | 0.37 | -0.50 | -0.79 | -0.64 | -0.56 |
| MgO | -0.92 | 0.82 | 0.81 | 0.84 | 0.88 | 0.65 | 1.00 | 0.80 | 0.61 | 0.44 | 0.74 | 0.17 | 0.18 | 0.31 | 0.26 | 0.43 | 0.46 | 0.74 | -0.82 | -0.84 | -0.67 | -0.59 |
| CaO | -0.58 | 0.85 | 0.32 | 0.80 | 0.66 | 0.38 | 0.80 | 1.00 | 0.49 | -0.15 | 0.86 | -0.18 | -0.17 | -0.01 | -0.08 | 0.13 | 0.90 | 0.40 | -0.99 | -0.38 | -0.70 | -0.79 |
| Na2O | -0.45 | 0.18 | 0.49 | 0.15 | 0.23 | 0.08 | 0.61 | 0.49 | 1.00 | 0.50 | 0.17 | -0.53 | -0.53 | -0.46 | -0.49 | -0.38 | 0.27 | 0.91 | -0.44 | -0.52 | 0.03 | 0.11 |
| K2O | -0.62 | 0.01 | 0.85 | 0.08 | 0.38 | 0.46 | 0.44 | -0.15 | 0.50 | 1.00 | -0.10 | 0.32 | 0.32 | 0.30 | 0.31 | 0.28 | -0.56 | 0.78 | 0.10 | -0.82 | 0.07 | 0.34 |
| P2O5 | -0.70 | 0.98 | 0.43 | 0.95 | 0.85 | 0.72 | 0.74 | 0.86 | 0.17 | -0.10 | 1.00 | 0.25 | 0.25 | 0.40 | 0.35 | 0.52 | 0.71 | 0.21 | -0.93 | -0.47 | -0.85 | -0.93 |
| (CIW) | -0.46 | 0.35 | 0.48 | 0.44 | 0.56 | 0.68 | 0.17 | -0.18 | -0.53 | 0.32 | 0.25 | 1.00 | 1.00 | 0.99 | 0.99 | 0.95 | -0.39 | -0.15 | 0.06 | -0.45 | -0.45 | -0.31 |
| (CPA) | -0.46 | 0.35 | 0.48 | 0.44 | 0.56 | 0.68 | 0.18 | -0.17 | -0.53 | 0.32 | 0.25 | 1.00 | 1.00 | 0.99 | 0.99 | 0.95 | -0.38 | -0.15 | 0.06 | -0.45 | -0.46 | -0.32 |
| (CIA) | -0.57 | 0.50 | 0.54 | 0.58 | 0.68 | 0.76 | 0.31 | -0.01 | -0.46 | 0.30 | 0.40 | 0.99 | 0.99 | 1.00 | 1.00 | 0.99 | -0.25 | -0.08 | -0.10 | -0.52 | -0.57 | -0.45 |
| (PIA) | -0.53 | 0.44 | 0.52 | 0.53 | 0.64 | 0.74 | 0.26 | -0.08 | -0.49 | 0.31 | 0.35 | 0.99 | 0.99 | 1.00 | 1.00 | 0.98 | -0.30 | -0.11 | -0.04 | -0.50 | -0.53 | -0.40 |
| (CIX) | -0.65 | 0.61 | 0.58 | 0.69 | 0.77 | 0.81 | 0.43 | 0.13 | -0.38 | 0.28 | 0.52 | 0.95 | 0.95 | 0.99 | 0.98 | 1.00 | -0.11 | -0.02 | -0.24 | -0.57 | -0.67 | -0.56 |
| (ICV) | -0.18 | 0.64 | -0.13 | 0.58 | 0.32 | 0.04 | 0.46 | 0.90 | 0.27 | -0.56 | 0.71 | -0.39 | -0.38 | -0.25 | -0.30 | -0.11 | 1.00 | 0.03 | -0.85 | 0.06 | -0.53 | -0.75 |
| (WIP) | -0.71 | 0.28 | 0.80 | 0.29 | 0.47 | 0.37 | 0.74 | 0.40 | 0.91 | 0.78 | 0.21 | -0.15 | -0.15 | -0.08 | -0.11 | -0.02 | 0.03 | 1.00 | -0.39 | -0.80 | -0.09 | 0.08 |
| (W) | 0.65 | -0.91 | -0.39 | -0.87 | -0.74 | -0.50 | -0.82 | -0.99 | -0.44 | 0.10 | -0.93 | 0.06 | 0.06 | -0.10 | -0.04 | -0.24 | -0.85 | -0.39 | 1.00 | 0.45 | 0.77 | 0.84 |
| SiO2/Al2O3 | 0.96 | -0.58 | -1.00 | -0.64 | -0.84 | -0.79 | -0.84 | -0.38 | -0.52 | -0.82 | -0.47 | -0.45 | -0.45 | -0.52 | -0.50 | -0.57 | 0.06 | -0.80 | 0.45 | 1.00 | 0.45 | 0.26 |
| K2O/Al2O3 | 0.63 | -0.91 | -0.42 | -0.88 | -0.80 | -0.64 | -0.67 | -0.70 | 0.03 | 0.07 | -0.85 | -0.45 | -0.46 | -0.57 | -0.53 | -0.67 | -0.53 | -0.09 | 0.77 | 0.45 | 1.00 | 0.88 |
| Al2O3/TiO2 | 0.51 | -0.92 | -0.21 | -0.91 | -0.74 | -0.56 | -0.59 | -0.79 | 0.11 | 0.34 | -0.93 | -0.31 | -0.32 | -0.45 | -0.40 | -0.56 | -0.75 | 0.08 | 0.84 | 0.26 | 0.88 | 1.00 |
Correlation diagrams¶
# Correlation diagram of subcategory ('Manasbal_Lake')
corr_ml = make_subplots(rows=1, cols=8)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_ml.add_trace(go.Scatter(
x=data_ml['Al2O3/TiO2'],
y=data_ml['(W)'],
mode = 'markers'
), row=1, col=8)
corr_ml.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Manasbal_Lake", showlegend=False, hovermode="x")
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_ml.update_yaxes(title_text="CIW", row=1, col=1)
corr_ml.update_yaxes(title_text="CPA", row=1, col=2)
corr_ml.update_yaxes(title_text="CIA", row=1, col=3)
corr_ml.update_yaxes(title_text="PIA", row=1, col=4)
corr_ml.update_yaxes(title_text="CIX", row=1, col=5)
corr_ml.update_yaxes(title_text="ICV", row=1, col=6)
corr_ml.update_yaxes(title_text="WIP", row=1, col=7)
corr_ml.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_ml.write_html("Correlation diagram of chemical weathering indices of Manasbal_Lake.html") # for 'html' version
# iplot(corr_ml, image='svg', filename='Correlation diagram of chemical weathering indices of Manasbal_Lake', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_ml, image='jpeg', filename='Correlation diagram of chemical weathering indices of Manasbal_Lake', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (Surma_Group)
corr_sg = make_subplots(rows=1, cols=8)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_sg.add_trace(go.Scatter(
x=data_sg['Al2O3/TiO2'],
y=data_sg['(W)'],
mode = 'markers'
), row=1, col=8)
corr_sg.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Surma_Group", showlegend=False, hovermode="x")
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_sg.update_yaxes(title_text="CIW", row=1, col=1)
corr_sg.update_yaxes(title_text="CPA", row=1, col=2)
corr_sg.update_yaxes(title_text="CIA", row=1, col=3)
corr_sg.update_yaxes(title_text="PIA", row=1, col=4)
corr_sg.update_yaxes(title_text="CIX", row=1, col=5)
corr_sg.update_yaxes(title_text="ICV", row=1, col=6)
corr_sg.update_yaxes(title_text="WIP", row=1, col=7)
corr_sg.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_sg.write_html("Correlation diagram of chemical weathering indices of Surma_Group.html") # for 'html' version
# iplot(corr_sg, image='svg', filename='Correlation diagram of chemical weathering indices of Surma_Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_sg, image='jpeg', filename='Correlation diagram of chemical weathering indices of Surma_Group', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (Barail_Group)
corr_bg = make_subplots(rows=1, cols=8)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_bg.add_trace(go.Scatter(
x=data_bg['Al2O3/TiO2'],
y=data_bg['(W)'],
mode = 'markers'
), row=1, col=8)
corr_bg.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Barail_Group", showlegend=False, hovermode="x")
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_bg.update_yaxes(title_text="CIW", row=1, col=1)
corr_bg.update_yaxes(title_text="CPA", row=1, col=2)
corr_bg.update_yaxes(title_text="CIA", row=1, col=3)
corr_bg.update_yaxes(title_text="PIA", row=1, col=4)
corr_bg.update_yaxes(title_text="CIX", row=1, col=5)
corr_bg.update_yaxes(title_text="ICV", row=1, col=6)
corr_bg.update_yaxes(title_text="WIP", row=1, col=7)
corr_bg.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_bg.write_html("Correlation diagram of chemical weathering indices of Barail_Group.html") # for 'html' version
# iplot(corr_bg, image='svg', filename='Correlation diagram of chemical weathering indices of Barail_Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_bg, image='jpeg', filename='Correlation diagram of chemical weathering indices of Barail_Group', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (channel_sediments)
corr_cs = make_subplots(rows=1, cols=8)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_cs.add_trace(go.Scatter(
x=data_cs['Al2O3/TiO2'],
y=data_cs['(W)'],
mode = 'markers'
), row=1, col=8)
corr_cs.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of channel_sediments", showlegend=False, hovermode="x")
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_cs.update_yaxes(title_text="CIW", row=1, col=1)
corr_cs.update_yaxes(title_text="CPA", row=1, col=2)
corr_cs.update_yaxes(title_text="CIA", row=1, col=3)
corr_cs.update_yaxes(title_text="PIA", row=1, col=4)
corr_cs.update_yaxes(title_text="CIX", row=1, col=5)
corr_cs.update_yaxes(title_text="ICV", row=1, col=6)
corr_cs.update_yaxes(title_text="WIP", row=1, col=7)
corr_cs.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_cs.write_html("Correlation diagram of chemical weathering indices of channel_sediments.html") # for 'html' version
# iplot(corr_cs, image='svg', filename='Correlation diagram of chemical weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_cs, image='jpeg', filename='Correlation diagram of chemical weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (overbank_sediments)
corr_os = make_subplots(rows=1, cols=8)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_os.add_trace(go.Scatter(
x=data_os['Al2O3/TiO2'],
y=data_os['(W)'],
mode = 'markers'
), row=1, col=8)
corr_os.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of overbank_sediments", showlegend=False, hovermode="x")
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_os.update_yaxes(title_text="CIW", row=1, col=1)
corr_os.update_yaxes(title_text="CPA", row=1, col=2)
corr_os.update_yaxes(title_text="CIA", row=1, col=3)
corr_os.update_yaxes(title_text="PIA", row=1, col=4)
corr_os.update_yaxes(title_text="CIX", row=1, col=5)
corr_os.update_yaxes(title_text="ICV", row=1, col=6)
corr_os.update_yaxes(title_text="WIP", row=1, col=7)
corr_os.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_os.write_html("Correlation diagram of chemical weathering indices of overbank_sediments.html") # for 'html' version
# iplot(corr_os, image='svg', filename='Correlation diagram of chemical weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_os, image='jpeg', filename='Correlation diagram of chemical weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (suspended_sediments)
corr_ss = make_subplots(rows=1, cols=8)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_ss.add_trace(go.Scatter(
x=data_ss['Al2O3/TiO2'],
y=data_ss['(W)'],
mode = 'markers'
), row=1, col=8)
corr_ss.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of suspended_sediments", showlegend=False, hovermode="x")
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_ss.update_yaxes(title_text="CIW", row=1, col=1)
corr_ss.update_yaxes(title_text="CPA", row=1, col=2)
corr_ss.update_yaxes(title_text="CIA", row=1, col=3)
corr_ss.update_yaxes(title_text="PIA", row=1, col=4)
corr_ss.update_yaxes(title_text="CIX", row=1, col=5)
corr_ss.update_yaxes(title_text="ICV", row=1, col=6)
corr_ss.update_yaxes(title_text="WIP", row=1, col=7)
corr_ss.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_ss.write_html("Correlation diagram of chemical weathering indices of suspended_sediments.html") # for 'html' version
# iplot(corr_ss, image='svg', filename='Correlation diagram of chemical weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_ss, image='jpeg', filename='Correlation diagram of chemical weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (Silty_clay)
corr_sc = make_subplots(rows=1, cols=8)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_sc.add_trace(go.Scatter(
x=data_sc['Al2O3/TiO2'],
y=data_sc['(W)'],
mode = 'markers'
), row=1, col=8)
corr_sc.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Silty_clay", showlegend=False, hovermode="x")
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_sc.update_yaxes(title_text="CIW", row=1, col=1)
corr_sc.update_yaxes(title_text="CPA", row=1, col=2)
corr_sc.update_yaxes(title_text="CIA", row=1, col=3)
corr_sc.update_yaxes(title_text="PIA", row=1, col=4)
corr_sc.update_yaxes(title_text="CIX", row=1, col=5)
corr_sc.update_yaxes(title_text="ICV", row=1, col=6)
corr_sc.update_yaxes(title_text="WIP", row=1, col=7)
corr_sc.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_sc.write_html("Correlation diagram of chemical weathering indices of Silty_clay.html") # for 'html' version
# iplot(corr_sc, image='svg', filename='Correlation diagram of chemical weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_sc, image='jpeg', filename='Correlation diagram of chemical weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'jpeg' version
# Correlation diagram of subcategory (Sand)
corr_s = make_subplots(rows=1, cols=8)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(CIW)'],
mode = 'markers'
), row=1, col=1)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(CPA)'],
mode = 'markers'
), row=1, col=2)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(CIA)'],
mode = 'markers'
), row=1, col=3)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(PIA)'],
mode = 'markers'
), row=1, col=4)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(CIX)'],
mode = 'markers'
), row=1, col=5)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(ICV)'],
mode = 'markers'
), row=1, col=6)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(WIP)'],
mode = 'markers'
), row=1, col=7)
corr_s.add_trace(go.Scatter(
x=data_s['Al2O3/TiO2'],
y=data_s['(W)'],
mode = 'markers'
), row=1, col=8)
corr_s.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Sand", showlegend=False, hovermode="x")
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)
corr_s.update_yaxes(title_text="CIW", row=1, col=1)
corr_s.update_yaxes(title_text="CPA", row=1, col=2)
corr_s.update_yaxes(title_text="CIA", row=1, col=3)
corr_s.update_yaxes(title_text="PIA", row=1, col=4)
corr_s.update_yaxes(title_text="CIX", row=1, col=5)
corr_s.update_yaxes(title_text="ICV", row=1, col=6)
corr_s.update_yaxes(title_text="WIP", row=1, col=7)
corr_s.update_yaxes(title_text="W", row=1, col=8)
# uncomment below lines of code to export the plot to your local machine
# corr_s.write_html("Correlation diagram of chemical weathering indices of Sand.html") # for 'html' version
# iplot(corr_s, image='svg', filename='Correlation diagram of chemical weathering indices of Sand', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_s, image='jpeg', filename='Correlation diagram of chemical weathering indices of Sand', image_width=1500, image_height=600) # for 'jpeg' version
SedWeathering layout design¶
app = dash.Dash()
# function for the table genereation
# user may change the argument ('max_rows') for the desired number of observation/rows (currently set to 10)
def generate_table(dataframe, max_rows=10):
return html.Table([
html.Thead(
html.Tr([html.Th(col) for col in dataframe.columns])
),
html.Tbody([
html.Tr([
html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
]) for i in range(min(len(dataframe), max_rows))
])
])
app.layout = html.Div(children=[
html.H1(
children='SedWeather',
style={
'textAlign': 'center'
}
),
html.H2(children='A web application framework to visualize Chemical Weathering of Clastic Sediments',
style={
'textAlign': 'center'
}),
html.H3(children='Major oxides with chemical weathering proxies'),
generate_table(data),
html.H3(children='Boxplot of weathering proxies'),
html.Div([
dcc.Tabs([
dcc.Tab(label='CIW variation', children=[
dcc.Graph(figure=box_CIW)
]),
dcc.Tab(label='CPA variation', children=[
dcc.Graph(figure=box_CPA)
]),
dcc.Tab(label='CIA variation', children=[
dcc.Graph(figure=box_CIA)
]),
dcc.Tab(label='PIA variation', children=[
dcc.Graph(figure=box_PIA)
]),
dcc.Tab(label='CIX variation', children=[
dcc.Graph(figure=box_CIX)
]),
dcc.Tab(label='ICV variation', children=[
dcc.Graph(figure=box_ICV)
]),
dcc.Tab(label='WIP variation', children=[
dcc.Graph(figure=box_WIP)
]),
dcc.Tab(label='W variation', children=[
dcc.Graph(figure=box_W)
])
]),
html.H3(children='scatter matrix of weathering indices'),
html.Div([
dcc.Tabs([
dcc.Tab(label='Manasbal lake', children=[
dcc.Graph(figure=scatter_ml)
]),
dcc.Tab(label='Surma Group', children=[
dcc.Graph(figure=scatter_sg)
]),
dcc.Tab(label='Barail Group', children=[
dcc.Graph(figure=scatter_bg)
]),
dcc.Tab(label='channel sediments', children=[
dcc.Graph(figure=scatter_cs)
]),
dcc.Tab(label='overbank sediments', children=[
dcc.Graph(figure=scatter_os)
]),
dcc.Tab(label='suspended sediments', children=[
dcc.Graph(figure=scatter_ss)
]),
dcc.Tab(label='Silty clay', children=[
dcc.Graph(figure=scatter_sc)
]),
dcc.Tab(label='Sand', children=[
dcc.Graph(figure=scatter_s)
])
]),
html.H3(children='Correlation diagrams'),
html.Div([
dcc.Tabs([
dcc.Tab(label='Manasbal Lake', children=[
dcc.Graph(figure=corr_ml)
]),
dcc.Tab(label='Surma Group', children=[
dcc.Graph(figure=corr_sg)
]),
dcc.Tab(label='Barail Group', children=[
dcc.Graph(figure=corr_bg)
]),
dcc.Tab(label='channel sediments', children=[
dcc.Graph(figure=corr_cs)
]),
dcc.Tab(label='overbank sediments', children=[
dcc.Graph(figure=corr_os)
]),
dcc.Tab(label='suspended sediments', children=[
dcc.Graph(figure=corr_ss)
]),
dcc.Tab(label='Silty clay', children=[
dcc.Graph(figure=corr_sc)
]),
dcc.Tab(label='Sand', children=[
dcc.Graph(figure=corr_s)
])
]),
html.H3(children='user-defined variation plot'),
html.Div([
dcc.Graph(id='graph'),
html.Label([
"oxide/weathering index",
dcc.Dropdown(
id='column-dropdown', clearable=False,
value = data.columns[0], options=[
{'label': c, 'value': c}
for c in data.columns
])
]),
]),
html.H3(children='Compositional space diagrams'),
html.H4(children='A-CN-K Compositional space diagram'),
html.H5(children='category and subcategory plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='category plot', children=[
dcc.Graph(figure=ACNK_cat)
]),
dcc.Tab(label='subcategory plot', children=[
dcc.Graph(figure=ACNK_subcat)
])
]),
html.H5(children='user-defined plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='upper_continental_crust', children=[
dcc.Graph(figure=ACNK_upper_continental_crust)
]),
dcc.Tab(label='igneous_composite', children=[
dcc.Graph(figure=ACNK_igneous_composite)
]),
dcc.Tab(label='shale_composite', children=[
dcc.Graph(figure=ACNK_shale_composite)
]),
dcc.Tab(label='UCC_rs_r', children=[
dcc.Graph(figure=ACNK_UCC_rs_r)
]),
dcc.Tab(label='UCC_ml', children=[
dcc.Graph(figure=ACNK_UCC_ml)
]),
dcc.Tab(label='UCC_sg_bg', children=[
dcc.Graph(figure=ACNK_UCC_sg_bg)
]),
dcc.Tab(label='UCC_cs_os_ss', children=[
dcc.Graph(figure=ACNK_UCC_cs_os_ss)
]),
dcc.Tab(label='UCC_sc_s', children=[
dcc.Graph(figure=ACNK_UCC_sc_s)
])
]),
html.H4(children='M-F-W Compositional space diagram'),
html.H5(children='category and subcategory plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='category plot', children=[
dcc.Graph(figure=MFW_cat)
]),
dcc.Tab(label='subcategory plot', children=[
dcc.Graph(figure=MFW_subcat)
])
]),
html.H5(children='user-defined plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='upper_continental_crust', children=[
dcc.Graph(figure=MFW_upper_continental_crust)
]),
dcc.Tab(label='igneous_composite', children=[
dcc.Graph(figure=MFW_igneous_composite)
]),
dcc.Tab(label='shale_composite', children=[
dcc.Graph(figure=MFW_shale_composite)
]),
dcc.Tab(label='UCC_rs_r', children=[
dcc.Graph(figure=MFW_UCC_rs_r)
]),
dcc.Tab(label='UCC_ml', children=[
dcc.Graph(figure=MFW_UCC_ml)
]),
dcc.Tab(label='UCC_sg_bg', children=[
dcc.Graph(figure=MFW_UCC_sg_bg)
]),
dcc.Tab(label='UCC_cs_os_ss', children=[
dcc.Graph(figure=MFW_UCC_cs_os_ss)
]),
dcc.Tab(label='UCC_sc_s', children=[
dcc.Graph(figure=MFW_UCC_sc_s)
])
]),
html.H4(children='A-CNK-FM Compositional space diagram'),
html.H5(children='category and subcategory plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='category plot', children=[
dcc.Graph(figure=ACNKFM_cat)
]),
dcc.Tab(label='subcategory plot', children=[
dcc.Graph(figure=ACNKFM_subcat)
])
]),
html.H5(children='user-defined plots'),
html.Div([
dcc.Tabs([
dcc.Tab(label='upper_continental_crust', children=[
dcc.Graph(figure=ACNKFM_upper_continental_crust)
]),
dcc.Tab(label='igneous_composite', children=[
dcc.Graph(figure=ACNKFM_igneous_composite)
]),
dcc.Tab(label='shale_composite', children=[
dcc.Graph(figure=ACNKFM_shale_composite)
]),
dcc.Tab(label='UCC_rs_r', children=[
dcc.Graph(figure=ACNKFM_UCC_rs_r)
]),
dcc.Tab(label='UCC_ml', children=[
dcc.Graph(figure=ACNKFM_UCC_ml)
]),
dcc.Tab(label='UCC_sg_bg', children=[
dcc.Graph(figure=ACNKFM_UCC_sg_bg)
]),
dcc.Tab(label='UCC_cs_os_ss', children=[
dcc.Graph(figure=ACNKFM_UCC_cs_os_ss)
]),
dcc.Tab(label='UCC_sc_s', children=[
dcc.Graph(figure=ACNKFM_UCC_sc_s)
])
])
])
])
])
])
])
])
])
])
])
])
# Define callback to update graph
@app.callback(
Output('graph', 'figure'),
[Input("column-dropdown", "value")]
)
def update_figure(column):
return px.scatter(
data, x="sample", y=column, hover_name="sample",
color="subcategory", symbol="subsubcategory",
render_mode="webgl", title="Variation Plot",
height=600, width=1500, color_discrete_sequence=px.colors.qualitative.Antique
)
if __name__ == '__main__':
app.run_server(port = 4050)
Dash is running on http://127.0.0.1:4050/ * Serving Flask app "__main__" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off
* Running on http://127.0.0.1:4050/ (Press CTRL+C to quit) 127.0.0.1 - - [01/Nov/2021 12:37:20] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:20] "GET /_dash-dependencies HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_favicon.ico?v=1.21.0 HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-layout HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-graph.js HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-dropdown.js HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "POST /_dash-update-component HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-plotlyjs.js HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:37:47] "POST /_dash-update-component HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:39:00] "POST /_dash-update-component HTTP/1.1" 200 - 127.0.0.1 - - [01/Nov/2021 12:39:11] "POST /_dash-update-component HTTP/1.1" 200 -